INSERT ABSTRACT
Our final taxa used in this anaylsis: - included from Bacteria, Archaea, Eukaryota and viruses - removed Chordata, Arthropoda, Cnidaria, Porifera, Echinodermata, Streptophyta, Platyhelminthes because they are implausible in our biological system (pig gut microbiome) - remove genera/phyla that have more than 20 zeroes/33.33% missing values across our dataset of n=60
In the end: Our final dataset has 45 phyla and 755 genera.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
if (!requireNamespace("devtools", quietly = TRUE))
install.packages('devtools')
BiocManager::install("ALDEx2")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'ALDEx2'
BiocManager::install("phyloseq")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'phyloseq'
remotes::install_github("cpauvert/psadd")
BiocManager::install("multtest")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'multtest'
library(devtools)
devtools::install_github("gauravsk/ranacapa")
# analysis packages
library(ALDEx2) # for univariate analysis
library(rstatix) # for ANOVA
library(vegan) # for beta and alpha diversity
## Warning: package 'permute' was built under R version 4.0.5
library(phyloseq) # for krona plots and rarefaction curves
library(psadd) # additions to phyloseq package for microbiome analysis
library(ranacapa) # Utility Functions for Simple Environmental Visualizations
# functionality packages
library(data.table) # for nicer transposing
library(here) # for directory management
library(knitr) # for knitting and for kable()
library(tidyverse) # for wrangling and plotting
## Warning: package 'tidyr' was built under R version 4.0.5
## Warning: package 'dplyr' was built under R version 4.0.5
library(readxl) # for reading Excel files
Some of our analyses include permutations, so let’s set a seed so we get consistent results each time we run.
set.seed(2021) # hoping this seed is better than 2020 :)
Input files can be found as supplementary information in:
The data read in chunk below enables loading our data without any outside-of-R handling. In “Metadata” tab of Supplementary Information.
# upload metadata
AllSamples.Metadata <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
sheet = "TableS2.SampleMetadata")
str(AllSamples.Metadata)
## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
## $ Sample_Name : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
## $ Pig : num [1:60] 6 8 3 14 5 18 16 10 2 18 ...
## $ Diet : chr [1:60] "Control" "Control" "Control" "Tomato" ...
## $ Time_Point : chr [1:60] "Day 14" "Day 0" "Day 14" "Day 7" ...
## $ Diet_By_Time_Point: chr [1:60] "Control Day 14" "Control Day 0" "Control Day 14" "Tomato Day 7" ...
# convert Pig, Diet, Time_Point, Diet_By_Time_Point to factors
# and set levels/order
AllSamples.Metadata$Pig <- as.factor(AllSamples.Metadata$Pig)
AllSamples.Metadata$Diet <- as.factor(AllSamples.Metadata$Diet)
AllSamples.Metadata$Time_Point <- factor(AllSamples.Metadata$Time_Point,
levels = c("Day 0", "Day 7", "Day 14"))
AllSamples.Metadata$Diet_By_Time_Point <-
factor(AllSamples.Metadata$Diet_By_Time_Point,
levels = c("Control Day 0",
"Control Day 7",
"Control Day 14",
"Tomato Day 0",
"Tomato Day 7",
"Tomato Day 14"))
# check
str(AllSamples.Metadata)
## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
## $ Sample_Name : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
## $ Pig : Factor w/ 20 levels "1","2","3","4",..: 6 8 3 14 5 18 16 10 2 18 ...
## $ Diet : Factor w/ 2 levels "Control","Tomato": 1 1 1 2 1 2 2 1 1 2 ...
## $ Time_Point : Factor w/ 3 levels "Day 0","Day 7",..: 3 1 3 2 2 2 2 2 1 1 ...
## $ Diet_By_Time_Point: Factor w/ 6 levels "Control Day 0",..: 3 1 3 5 2 5 5 2 1 4 ...
Read in genera level data, annotated from MG-RAST. In “Genera” tab of Supplementary Information.
Genus.AllSamples.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
sheet = "TableS4.Genera")
str(Genus.AllSamples.Counts)
## tibble [1,085 × 66] (S3: tbl_df/tbl/data.frame)
## $ domain : chr [1:1085] "Viruses" "Bacteria" "Eukaryota" "Bacteria" ...
## $ phylum : chr [1:1085] "unclassified (derived from Viruses)" "Firmicutes" "unclassified (derived from Eukaryota)" "Cyanobacteria" ...
## $ class : chr [1:1085] "unclassified (derived from Viruses)" "Bacilli" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
## $ order : chr [1:1085] "Caudovirales" "Lactobacillales" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
## $ family : chr [1:1085] "Podoviridae" "Aerococcaceae" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
## $ genus : chr [1:1085] "AHJD-like viruses" "Abiotrophia" "Acanthamoeba" "Acaryochloris" ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:1085] 29 5067 0 271 1988 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day0 : num [1:1085] 0 5661 0 416 2981 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:1085] 153 4117 0 267 2071 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7 : num [1:1085] 0 1576 1 131 1012 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day7 : num [1:1085] 14 3708 0 230 1991 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7 : num [1:1085] 1 1159 0 146 585 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7 : num [1:1085] 2 2495 0 133 1538 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:1085] 0 1636 0 141 812 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day0 : num [1:1085] 0 4534 0 338 2670 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0 : num [1:1085] 0 2964 1 272 1665 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:1085] 0 3197 0 264 1411 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day0 : num [1:1085] 0 2513 0 263 1652 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:1085] 342 4231 0 274 1795 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0 : num [1:1085] 0 3101 0 237 2160 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0 : num [1:1085] 0 3274 0 228 1729 ...
## $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:1085] 6 1424 0 83 683 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:1085] 131 3337 0 328 1722 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:1085] 86 3383 0 238 1976 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7 : num [1:1085] 0 1849 0 120 940 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:1085] 76 3864 0 363 2395 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day7 : num [1:1085] 1 5590 0 306 4493 ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day7 : num [1:1085] 0 3120 0 201 1273 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0 : num [1:1085] 0 2599 0 190 1451 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0 : num [1:1085] 1 1453 0 70 846 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:1085] 67 2906 0 248 1870 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:1085] 12 973 0 79 542 16 186 0 185 82 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0 : num [1:1085] 0 3682 0 211 2232 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day0 : num [1:1085] 2 2717 1 160 1547 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7 : num [1:1085] 0 375 0 31 227 7 69 0 89 29 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7 : num [1:1085] 0 2158 0 159 1774 ...
## $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0 : num [1:1085] 0 1409 0 197 762 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:1085] 89 1059 0 81 580 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0 : num [1:1085] 1 3634 0 207 2188 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:1085] 106 6111 0 386 3446 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day7 : num [1:1085] 0 3815 1 190 1775 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7 : num [1:1085] 1 1126 0 67 974 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0 : num [1:1085] 0 3134 0 224 2207 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7 : num [1:1085] 0 2376 0 144 1437 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:1085] 0 1079 0 61 469 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:1085] 47 926 0 61 486 18 205 0 193 41 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day0 : num [1:1085] 0 5545 0 310 2638 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:1085] 102 3677 0 270 1919 ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day0 : num [1:1085] 4 2687 0 200 1640 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0 : num [1:1085] 0 2959 0 176 1599 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:1085] 3 973 0 98 609 34 222 0 255 149 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:1085] 11 1075 0 86 446 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day7 : num [1:1085] 0 1587 0 103 654 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day7 : num [1:1085] 0 1709 0 165 1059 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:1085] 0 1021 0 74 685 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day7 : num [1:1085] 12 3035 0 259 1579 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:1085] 17 1660 0 140 892 35 366 0 410 224 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:1085] 19 2138 0 129 1334 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7 : num [1:1085] 0 1699 0 121 645 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:1085] 14 3895 0 338 2158 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day0 : num [1:1085] 0 4578 0 367 2356 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day0 : num [1:1085] 0 4842 0 274 2894 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day0 : num [1:1085] 0 4439 0 261 2733 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day7 : num [1:1085] 0 703 0 54 384 6 159 0 200 45 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day7 : num [1:1085] 6 4833 0 347 3158 ...
## $ ShotgunWGS-TomatoPig17GutMicrobime-Day7 : num [1:1085] 0 1713 0 136 782 ...
These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.
Genus.Counts.Filt <- Genus.AllSamples.Counts %>%
filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" ,
phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
phylum != "Platyhelminthes")
Transpose.
Genus.Counts.Filt.t <- as.tibble(t(Genus.Counts.Filt))
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
# make genus colnames
colnames(Genus.Counts.Filt.t) <- Genus.Counts.Filt.t[6,]
# remove domain, phylum, class, order, family
GenusOnly.Counts.Filt.t <- Genus.Counts.Filt.t[7:66,]
# convert character to numeric
GenusOnly.Counts.Filt.t <- as.data.frame(apply((GenusOnly.Counts.Filt.t), 2, as.numeric))
str(GenusOnly.Counts.Filt.t[,1:5])
## 'data.frame': 60 obs. of 5 variables:
## $ AHJD-like viruses: num 29 0 153 0 14 1 2 0 0 0 ...
## $ Abiotrophia : num 5067 5661 4117 1576 3708 ...
## $ Acanthamoeba : num 0 0 0 1 0 0 0 0 0 1 ...
## $ Acaryochloris : num 271 416 267 131 230 146 133 141 338 272 ...
## $ Acetivibrio : num 1988 2981 2071 1012 1991 ...
# add back sample names as column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
mutate(Sample_Name = AllSamples.Metadata$Sample_Name)
# move Sample_Name to first column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
relocate(Sample_Name)
kable(head(GenusOnly.Counts.Filt.t))
| Sample_Name | AHJD-like viruses | Abiotrophia | Acanthamoeba | Acaryochloris | Acetivibrio | Acetobacter | Acetohalobium | Acholeplasma | Achromobacter | Acidaminococcus | Acidilobus | Acidimicrobium | Acidiphilium | Acidithiobacillus | Acidobacterium | Acidothermus | Acidovorax | Aciduliprofundum | Acinetobacter | Actinobacillus | Actinomyces | Actinosynnema | Aerococcus | Aeromicrobium | Aeromonas | Aeropyrum | Afipia | Aggregatibacter | Agrobacterium | Ahrensia | Ajellomyces | Akkermansia | Albidiferax | Alcanivorax | Algoriphagus | Alicycliphilus | Alicyclobacillus | Aliivibrio | Alistipes | Alkalilimnicola | Alkaliphilus | Allochromatium | Allomyces | Alphabaculovirus | Alphatorquevirus | Alteromonas | Aminobacterium | Aminomonas | Ammonifex | Amycolatopsis | Anabaena | Anaerobaculum | Anaerococcus | Anaerofustis | Anaeromyxobacter | Anaerostipes | Anaerotruncus | Anaplasma | Anoxybacillus | Aquifex | Arcanobacterium | Archaeoglobus | Arcobacter | Aromatoleum | Arthrobacter | Arthroderma | Arthrospira | Ascovirus | Asfivirus | Aspergillus | Asticcacaulis | Atopobium | Aurantimonas | Aureococcus | Avibacterium | Avipoxvirus | Azoarcus | Azorhizobium | Azospirillum | Azotobacter | Babesia | Bacillus | Bacteroides | Bartonella | Basfia | Basidiobolus | Batrachochytrium | Bdellomicrovirus | Bdellovibrio | Beggiatoa | Beijerinckia | Bermanella | Betabaculovirus | Betaentomopoxvirus | Betaretrovirus | Beutenbergia | Bicaudavirus | Bifidobacterium | Bigelowiella | Blastocystis | Blastopirellula | Blattabacterium | Blautia | Bocavirus | Boothiomyces | Bordetella | Borrelia | Botryotinia | Bpp-1-like viruses | Brachybacterium | Brachyspira | Bradyrhizobium | Brevibacillus | Brevibacterium | Brevundimonas | Brucella | Brugia | Bryopsis | Buchnera | Bulleidia | Burkholderia | Butyrivibrio | Caenorhabditis | Cafeteria | Caldanaerobacter | Caldicellulosiruptor | Calditerrivibrio | Caldivirga | Caminibacter | Campylobacter | Candida | Candidatus Accumulibacter | Candidatus Amoebophilus | Candidatus Azobacteroides | Candidatus Blochmannia | Candidatus Carsonella | Candidatus Cloacamonas | Candidatus Desulforudis | Candidatus Hamiltonella | Candidatus Hodgkinia | Candidatus Korarchaeum | Candidatus Koribacter | Candidatus Liberibacter | Candidatus Pelagibacter | Candidatus Phytoplasma | Candidatus Protochlamydia | Candidatus Puniceispirillum | Candidatus Regiella | Candidatus Riesia | Candidatus Solibacter | Candidatus Sulcia | Candidatus Zinderia | Capnocytophaga | Carboxydothermus | Cardiobacterium | Carnobacterium | Catenibacterium | Catenulispora | Catonella | Caulobacter | Cavemovirus | Cellulomonas | Cellulosilyticum | Cellvibrio | Cenarchaeum | Chaetomium | Chelativorans | Chitinophaga | Chlamydia | Chlamydiamicrovirus | Chlamydomonas | Chlamydophila | Chlorella | Chloriridovirus | Chlorobaculum | Chlorobium | Chloroflexus | Chloroherpeton | Chlorovirus | Chondrus | Chromera | Chromobacterium | Chromohalobacter | Chryseobacterium | Chrysodidymus | Chthoniobacter | Citreicella | Citrobacter | Citromicrobium | Cladochytrium | Clavibacter | Clavispora | Clostridium | Coccidioides | Coccolithovirus | Coelomomyces | Collimonas | Collinsella | Colwellia | Comamonas | Conexibacter | Congregibacter | Conidiobolus | Coprinopsis | Coprobacillus | Coprococcus | Coprothermobacter | Coraliomargarita | Corynebacterium | Coxiella | Crinivirus | Croceibacter | Crocosphaera | Cronobacter | Cryptobacterium | Cryptomonas | Cryptosporidium | Cupriavidus | Cyanidioschyzon | Cyanidium | Cyanobium | Cyanophora | Cyanothece | Cylindrospermopsis | Cylindrospermum | Cyprinivirus | Cytomegalovirus | Cytophaga | Debaryomyces | Dechloromonas | Deferribacter | Dehalococcoides | Dehalogenimonas | Deinococcus | Delftia | Denitrovibrio | Dependovirus | Dermacoccus | Desulfarculus | Desulfatibacillum | Desulfitobacterium | Desulfobacterium | Desulfococcus | Desulfohalobium | Desulfomicrobium | Desulfonatronospira | Desulfotalea | Desulfotomaculum | Desulfovibrio | Desulfurispirillum | Desulfurivibrio | Desulfurococcus | Desulfuromonas | Dethiobacter | Dethiosulfovibrio | Dialister | Dichelobacter | Dickeya | Dictyoglomus | Dictyostelium | Dinoroseobacter | Dokdonia | Dorea | Durinskia | Dyadobacter | Ectocarpus | Edwardsiella | Eggerthella | Ehrlichia | Eikenella | Eimeria | Elusimicrobium | Emericella | Emiliania | Encephalitozoon | Endoriftia | Enhydrobacter | Entamoeba | Enterobacter | Enterococcus | Enterocytozoon | Entomophthora | Epsilon15-like viruses | Epulopiscium | Eremococcus | Eremothecium | Erwinia | Erysipelothrix | Erythrobacter | Escherichia | Ethanoligenens | Eubacterium | Euglena | Exiguobacterium | Faecalibacterium | Ferrimonas | Ferroglobus | Ferroplasma | Fervidobacterium | Fibrobacter | Filifactor | Filobasidiella | Finegoldia | Flavobacterium | Floydiella | Fluoribacter | Francisella | Frankia | Fucus | Fulvimarina | Fusobacterium | Gallionella | Gammaretrovirus | Gardnerella | Gemella | Gemmata | Gemmatimonas | Geobacillus | Geobacter | Geodermatophilus | Giardia | Gibberella | Glaciecola | Gloeobacter | Gluconacetobacter | Gluconobacter | Gordonia | Gracilaria | Gracilariopsis | Gramella | Granulibacter | Granulicatella | Guillardia | Haemophilus | Hafnia | Hahella | Halalkalicoccus | Halanaerobium | Haliangium | Haloarcula | Halobacterium | Haloferax | Halogeometricum | Halomicrobium | Halomonas | Haloquadratum | Halorhabdus | Halorhodospira | Halorubrum | Haloterrigena | Halothermothrix | Halothiobacillus | Harpochytrium | Helicobacter | Helicosporidium | Heliobacterium | Hemiselmis | Herbaspirillum | Herminiimonas | Herpetosiphon | Heterosigma | Hirschia | Histophilus | Hoeflea | Holdemania | Hyaloraphidium | Hydrogenivirga | Hydrogenobacter | Hydrogenobaculum | Hyperthermus | Hyphomicrobium | Hyphomonas | Hypocrea | Ichnovirus | Idiomarina | Ignicoccus | Ignisphaera | Ilyobacter | Inovirus | Intrasporangium | Iridovirus | Janibacter | Jannaschia | Janthinobacterium | Jonesia | Jonquetella | Kangiella | Ketogulonicigenium | Kineococcus | Kingella | Klebsiella | Kluyveromyces | Kocuria | Kordia | Kosmotoga | Kribbella | Kryptoperidinium | Ktedonobacter | Kytococcus | L5-like viruses | LUZ24-like viruses | Labrenzia | Laccaria | Lachancea | Lachnum | Lactobacillus | Lactococcus | Lambda-like viruses | Laminaria | Laribacter | Lawsonia | Leadbetterella | Leeuwenhoekiella | Legionella | Leifsonia | Leishmania | Lentisphaera | Leotia | Leptosira | Leptospira | Leptospirillum | Leptothrix | Leptotrichia | Leuconostoc | Limnobacter | Listeria | Listonella | Loa | Lodderomyces | Loktanella | Lutiella | Lymphocryptovirus | Lymphocystivirus | Lyngbya | Lysinibacillus | Macavirus | Macrococcus | Magnaporthe | Magnetococcus | Magnetospirillum | Malassezia | Malawimonas | Mannheimia | Maribacter | Maricaulis | Marinobacter | Marinococcus | Marinomonas | Mariprofundus | Maritimibacter | Marivirga | Megasphaera | Meiothermus | Mesoplasma | Mesorhizobium | Metallosphaera | Methanobrevibacter | Methanocaldococcus | Methanocella | Methanococcoides | Methanococcus | Methanocorpusculum | Methanoculleus | Methanohalobium | Methanohalophilus | Methanoplanus | Methanopyrus | Methanoregula | Methanosaeta | Methanosarcina | Methanosphaera | Methanosphaerula | Methanospirillum | Methanothermobacter | Methanothermococcus | Methanothermus | Methylacidiphilum | Methylibium | Methylobacillus | Methylobacter | Methylobacterium | Methylocella | Methylococcus | Methylophaga | Methylosinus | Methylotenera | Methylovorus | Meyerozyma | Micrococcus | Microcoleus | Microcystis | Micromonas | Micromonospora | Microscilla | Mitsuokella | Mobiluncus | Molluscipoxvirus | Moniliophthora | Monomastix | Monosiga | Moorella | Moraxella | Moritella | Mu-like viruses | Mucilaginibacter | Mycobacterium | Mycoplasma | Myxococcus | N15-like viruses | N4-like viruses | Naegleria | Nakamurella | Nakaseomyces | Nanoarchaeum | Natranaerobius | Natrialba | Natronomonas | Nautilia | Nectria | Neisseria | Neolecta | Neorickettsia | Neosartorya | Nephroselmis | Neptuniibacter | Neurospora | Nitratiruptor | Nitrobacter | Nitrococcus | Nitrosococcus | Nitrosomonas | Nitrosopumilus | Nitrosospira | Nitrospira | Nocardia | Nocardioides | Nocardiopsis | Nodularia | Nosema | Nostoc | Novosphingobium | Oceanibulbus | Oceanicaulis | Oceanicola | Oceanithermus | Oceanobacillus | Ochrobactrum | Ochromonas | Octadecabacter | Odontella | Oedogonium | Oenococcus | Oligotropha | Olsenella | Oltmannsiellopsis | Opitutus | Oribacterium | Orientia | Ornithobacterium | Orthopoxvirus | Oscillatoria | Oscillochloris | Ostreavirus | Ostreococcus | Oxalobacter | P1-like viruses | P2-like viruses | P22-like viruses | Paenibacillus | Paludibacter | Pantoea | Parabacteroides | Parachlamydia | Parachlorella | Paracoccidioides | Paracoccus | Paraglomus | Paramecium | Parascardovia | Parvibaculum | Parvularcula | Pasteurella | Paulinella | Paxillus | Pectobacterium | Pediococcus | Pedobacter | Pelagibaca | Pelobacter | Pelodictyon | Pelotomaculum | Penicillium | Peptoniphilus | Peptostreptococcus | Perkinsus | Persephonella | Petrotoga | Phaeobacter | Phaeodactylum | Phaeosphaeria | Phaeovirus | Phenylobacterium | Phi29-like viruses | PhiC31-like viruses | Phieco32-like viruses | Photobacterium | Photorhabdus | Physoderma | Phytophthora | Pichia | Picrophilus | Pirellula | Planctomyces | Planococcus | Plasmodium | Plectrovirus | Plesiocystis | Podospora | Polaribacter | Polaromonas | Polychytrium | Polynucleobacter | Polysphondylium | Porphyra | Porphyromonas | Postia | Prasinovirus | Prevotella | Prochlorococcus | Propionibacterium | Prosthecochloris | Proteromonas | Proteus | Prototheca | Providencia | Pseudendoclonium | Pseudoalteromonas | Pseudomonas | Pseudoramibacter | Pseudovibrio | Psychrobacter | Psychroflexus | Psychromonas | Pycnococcus | Pylaiella | Pyramidobacter | Pyramimonas | Pyrenophora | Pyrobaculum | Pyrococcus | Pythium | Ralstonia | Ranavirus | Raphidiopsis | Reclinomonas | Reinekea | Renibacterium | Rhadinovirus | Rhizobium | Rhodobacter | Rhodococcus | Rhodomicrobium | Rhodomonas | Rhodopirellula | Rhodopseudomonas | Rhodospirillum | Rhodothermus | Rhopalomyces | Rickettsia | Rickettsiella | Riemerella | Robiginitalea | Roseburia | Roseibium | Roseiflexus | Roseobacter | Roseomonas | Roseovarius | Rothia | Rozella | Rubrobacter | Rudivirus | Ruegeria | Ruminococcus | SP6-like viruses | SPO1-like viruses | SPbeta-like viruses | Saccharomonospora | Saccharomyces | Saccharophagus | Saccharopolyspora | Saccoglossus | Sagittula | Salinibacter | Salinispora | Salmonella | Sanguibacter | Saprolegnia | Scardovia | Scenedesmus | Scheffersomyces | Schizophyllum | Schizosaccharomyces | Sclerotinia | Sebaldella | Segniliparus | Selenomonas | Serratia | Shewanella | Shigella | Shuttleworthia | Sideroxydans | Simonsiella | Simplexvirus | Sinorhizobium | Slackia | Sodalis | Sorangium | Sphaerobacter | Sphingobacterium | Sphingobium | Sphingomonas | Sphingopyxis | Spirochaeta | Spiromicrovirus | Spiroplasma | Spirosoma | Sporosarcina | Stackebrandtia | Staphylococcus | Staphylothermus | Starkeya | Stenotrophomonas | Stigeoclonium | Stigmatella | Streptobacillus | Streptococcus | Streptomyces | Streptosporangium | Subdoligranulum | Sulfitobacter | Sulfolobus | Sulfuricurvum | Sulfurihydrogenibium | Sulfurimonas | Sulfurospirillum | Sulfurovum | Symbiobacterium | Synchytrium | Synechococcus | Synechocystis | Synedra | Syntrophobacter | Syntrophomonas | Syntrophothermus | Syntrophus | T1-like viruses | T4-like viruses | T5-like viruses | T7-like viruses | Talaromyces | Tectivirus | Teredinibacter | Terriglobus | Tetragenococcus | Tetrahymena | Thalassiosira | Thalassobium | Thauera | Theileria | Thermaerobacter | Thermanaerovibrio | Thermincola | Thermoanaerobacter | Thermoanaerobacterium | Thermobaculum | Thermobifida | Thermobispora | Thermococcus | Thermocrinis | Thermodesulfovibrio | Thermofilum | Thermomicrobium | Thermomonospora | Thermoplasma | Thermoproteus | Thermosediminibacter | Thermosinus | Thermosipho | Thermosphaera | Thermosynechococcus | Thermotoga | Thermus | Thioalkalivibrio | Thiobacillus | Thiomicrospira | Thiomonas | Tolumonas | Toxoplasma | Treponema | Trichodesmium | Trichomonas | Trichophyton | Trichoplax | Tropheryma | Truepera | Trypanosoma | Tsukamurella | Tuber | Turicibacter | Uncinocarpus | Ureaplasma | Ustilago | VP2-like phages | Vanderwaltozyma | Varicellovirus | Variovorax | Vaucheria | Veillonella | Verminephrobacter | Verrucomicrobium | Verticillium | Vibrio | Victivallis | Volvox | Vulcanisaeta | Waddlia | Weissella | Whispovirus | Wigglesworthia | Wolbachia | Wolinella | Xanthobacter | Xanthomonas | Xenorhabdus | Xylanimonas | Xylella | Yarrowia | Yatapoxvirus | Yersinia | Zunongwangia | Zygosaccharomyces | Zymomonas | c2-like viruses | phiKMV-like viruses | phiKZ-like viruses | unclassified (derived from Actinobacteria (class)) | unclassified (derived from Alicyclobacillaceae) | unclassified (derived from Alloherpesviridae) | unclassified (derived from Alphaproteobacteria) | unclassified (derived from Alteromonadales) | unclassified (derived from Bacteria) | unclassified (derived from Bacteroidetes) | unclassified (derived from Betaproteobacteria) | unclassified (derived from Burkholderiales) | unclassified (derived from Campylobacterales) | unclassified (derived from Candidatus Poribacteria) | unclassified (derived from Caudovirales) | unclassified (derived from Chromerida) | unclassified (derived from Chroococcales) | unclassified (derived from Clostridiales Family XI. Incertae Sedis) | unclassified (derived from Clostridiales) | unclassified (derived from Deltaproteobacteria) | unclassified (derived from Elusimicrobia) | unclassified (derived from Erysipelotrichaceae) | unclassified (derived from Euryarchaeota) | unclassified (derived from Flavobacteria) | unclassified (derived from Flavobacteriaceae) | unclassified (derived from Flavobacteriales) | unclassified (derived from Fuselloviridae) | unclassified (derived from Gammaproteobacteria) | unclassified (derived from Lachnospiraceae) | unclassified (derived from Marseillevirus family) | unclassified (derived from Methylophilales) | unclassified (derived from Mononegavirales) | unclassified (derived from Myoviridae) | unclassified (derived from Opitutaceae) | unclassified (derived from Pelagophyceae) | unclassified (derived from Phycodnaviridae) | unclassified (derived from Podoviridae) | unclassified (derived from Poxviridae) | unclassified (derived from Proteobacteria) | unclassified (derived from Rhodobacteraceae) | unclassified (derived from Rhodobacterales) | unclassified (derived from Rickettsiales) | unclassified (derived from Ruminococcaceae) | unclassified (derived from Siphoviridae) | unclassified (derived from Thermotogales) | unclassified (derived from Thiotrichales) | unclassified (derived from Verrucomicrobia subdivision 3) | unclassified (derived from Verrucomicrobiales) | unclassified (derived from Vibrionaceae) | unclassified (derived from Vibrionales) | unclassified (derived from Viruses) | unclassified (derived from other sequences) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShotgunWGS-ControlPig6GutMicrobiome-Day14 | 29 | 5067 | 0 | 271 | 1988 | 66 | 1036 | 779 | 192 | 50181 | 10 | 59 | 244 | 245 | 365 | 389 | 790 | 217 | 794 | 2621 | 1004 | 193 | 341 | 25 | 851 | 31 | 23 | 368 | 517 | 16 | 11 | 1474 | 342 | 255 | 171 | 81 | 1438 | 520 | 1983 | 413 | 13144 | 198 | 0 | 0 | 8 | 148 | 509 | 101 | 915 | 161 | 473 | 130 | 4600 | 1549 | 1311 | 4289 | 6643 | 22 | 1391 | 554 | 531 | 461 | 513 | 412 | 951 | 14 | 92 | 0 | 0 | 167 | 287 | 35624 | 112 | 0 | 0 | 0 | 286 | 190 | 165 | 192 | 8 | 27258 | 318931 | 222 | 744 | 0 | 0 | 1 | 334 | 81 | 96 | 27 | 0 | 0 | 0 | 406 | 0 | 40993 | 0 | 0 | 295 | 52 | 17707 | 0 | 0 | 947 | 291 | 19 | 46 | 301 | 5181 | 712 | 1770 | 279 | 126 | 276 | 35 | 1 | 86 | 2321 | 2463 | 40314 | 135 | 0 | 6815 | 8872 | 295 | 28 | 33 | 4599 | 42 | 203 | 196 | 702 | 51 | 0 | 63 | 1459 | 48 | 0 | 75 | 671 | 23 | 61 | 169 | 177 | 46 | 18 | 7 | 1612 | 14 | 0 | 2284 | 4439 | 72 | 232 | 4894 | 250 | 177 | 815 | 0 | 291 | 2656 | 581 | 15 | 30 | 274 | 2203 | 137 | 1 | 127 | 135 | 0 | 1 | 662 | 2359 | 907 | 468 | 4 | 0 | 0 | 402 | 350 | 328 | 0 | 85 | 13 | 456 | 16 | 0 | 408 | 17 | 297762 | 12 | 0 | 0 | 0 | 19115 | 257 | 119 | 324 | 143 | 0 | 26 | 1405 | 24605 | 336 | 203 | 6812 | 194 | 0 | 475 | 245 | 371 | 3943 | 1 | 26 | 847 | 8 | 25 | 12 | 5 | 1558 | 55 | 0 | 0 | 0 | 1635 | 32 | 358 | 555 | 3574 | 330 | 1030 | 221 | 644 | 0 | 31 | 309 | 591 | 14579 | 526 | 613 | 264 | 575 | 70 | 755 | 11294 | 4843 | 331 | 226 | 17 | 644 | 426 | 992 | 24138 | 330 | 453 | 1217 | 175 | 136 | 366 | 20388 | 0 | 1864 | 0 | 296 | 10647 | 64 | 28 | 0 | 386 | 54 | 0 | 2 | 13 | 32 | 99 | 615 | 10711 | 47 | 0 | 4 | 540 | 116 | 36 | 282 | 287 | 333 | 1977 | 17513 | 276925 | 3 | 2132 | 134155 | 132 | 126 | 95 | 785 | 4768 | 594 | 73 | 2960 | 4955 | 0 | 0 | 419 | 950 | 0 | 40 | 7250 | 131 | 1 | 2547 | 156 | 92 | 211 | 7035 | 5443 | 207 | 24 | 96 | 1 | 470 | 112 | 247 | 125 | 2 | 0 | 1415 | 162 | 478 | 28 | 1195 | 0 | 433 | 43 | 915 | 283 | 87 | 64 | 52 | 61 | 39 | 121 | 59 | 58 | 226 | 41 | 76 | 2226 | 98 | 0 | 2189 | 0 | 5259 | 1 | 147 | 222 | 519 | 4 | 122 | 474 | 31 | 13306 | 0 | 69 | 142 | 263 | 43 | 78 | 167 | 0 | 2 | 288 | 43 | 26 | 2406 | 0 | 149 | 0 | 111 | 128 | 225 | 334 | 271 | 109 | 68 | 359 | 34 | 785 | 38 | 261 | 115 | 371 | 216 | 0 | 140 | 162 | 1 | 0 | 61 | 13 | 29 | 0 | 178641 | 2714 | 65 | 0 | 145 | 335 | 1469 | 1324 | 556 | 216 | 85 | 102 | 0 | 2 | 516 | 1 | 217 | 2436 | 1674 | 31 | 4368 | 0 | 8 | 13 | 42 | 81 | 0 | 0 | 72 | 1157 | 0 | 503 | 58 | 579 | 640 | 26 | 0 | 381 | 784 | 137 | 482 | 0 | 565 | 47 | 88 | 536 | 39571 | 549 | 194 | 308 | 43 | 1912 | 417 | 179 | 400 | 1200 | 1296 | 383 | 128 | 136 | 276 | 105 | 284 | 269 | 1565 | 499 | 213 | 423 | 350 | 10 | 45 | 102 | 181 | 204 | 40 | 588 | 198 | 374 | 13 | 24 | 141 | 173 | 24 | 220 | 73 | 344 | 48 | 219 | 309 | 106534 | 5991 | 0 | 15 | 0 | 57 | 4305 | 92 | 50 | 0 | 657 | 1711 | 1430 | 576 | 0 | 2 | 32 | 220 | 71 | 4 | 1577 | 32 | 62 | 158 | 24 | 899 | 0 | 55 | 155 | 1 | 38 | 96 | 391 | 324 | 121 | 471 | 423 | 41 | 256 | 191 | 365 | 426 | 178 | 69 | 0 | 689 | 271 | 21 | 98 | 114 | 158 | 1728 | 274 | 0 | 53 | 8 | 1 | 776 | 144 | 25686 | 0 | 955 | 19432 | 45 | 0 | 0 | 42 | 79 | 0 | 92 | 514 | 0 | 1 | 0 | 7823 | 4818 | 526 | 24663 | 32 | 0 | 4 | 337 | 0 | 42 | 846 | 257 | 85 | 410 | 5 | 0 | 694 | 1736 | 3321 | 25 | 2655 | 1255 | 4478 | 50 | 1637 | 1371 | 32 | 222 | 677 | 20 | 36 | 39 | 0 | 188 | 0 | 0 | 0 | 797 | 335 | 0 | 63 | 8 | 57 | 158 | 375 | 1 | 138 | 0 | 118 | 17 | 901 | 425 | 0 | 192 | 0 | 21 | 8873 | 5 | 1 | 1029670 | 660 | 872 | 145 | 0 | 331 | 0 | 118 | 0 | 769 | 2976 | 5626 | 50 | 505 | 190 | 502 | 0 | 0 | 1711 | 0 | 18 | 97 | 443 | 0 | 581 | 0 | 25 | 6 | 203 | 141 | 0 | 814 | 604 | 739 | 83 | 4 | 603 | 937 | 533 | 648 | 0 | 215 | 9 | 491 | 732 | 93165 | 17 | 1503 | 364 | 38 | 136 | 332 | 0 | 817 | 0 | 298 | 145083 | 5 | 35 | 11 | 133 | 54 | 684 | 421 | 26 | 30 | 456 | 399 | 771 | 550 | 1 | 639 | 0 | 49 | 34 | 57 | 14 | 2577 | 34 | 36152 | 519 | 2720 | 237 | 5045 | 211 | 50 | 1 | 550 | 13349 | 101 | 434 | 540 | 443 | 82 | 290 | 263 | 2362 | 2 | 0 | 2579 | 0 | 178 | 4604 | 30 | 98 | 247 | 0 | 128 | 681 | 110465 | 1855 | 255 | 82351 | 48 | 158 | 140 | 608 | 359 | 274 | 232 | 3519 | 0 | 2287 | 497 | 0 | 833 | 3600 | 1217 | 980 | 0 | 22 | 0 | 0 | 46 | 0 | 172 | 226 | 11 | 34 | 61 | 21 | 165 | 16 | 946 | 739 | 2853 | 6464 | 3550 | 554 | 444 | 223 | 490 | 95 | 359 | 85 | 250 | 198 | 182 | 6 | 1924 | 2471 | 905 | 14 | 463 | 2161 | 570 | 311 | 295 | 238 | 99 | 350 | 43 | 3169 | 408 | 458 | 1 | 68 | 57 | 174 | 46 | 140 | 8 | 774 | 9 | 192 | 48 | 0 | 11 | 0 | 183 | 1 | 23909 | 272 | 65 | 7 | 2167 | 663 | 98 | 17 | 71 | 174 | 0 | 10 | 114 | 430 | 237 | 760 | 138 | 674 | 247 | 52 | 0 | 1199 | 1535 | 9 | 199 | 2 | 0 | 4 | 103 | 1173 | 0 | 56 | 32 | 580 | 946 | 11 | 365 | 48 | 26 | 64 | 0 | 33 | 421 | 12636 | 186 | 246 | 17626 | 340 | 602 | 668 | 261 | 0 | 409 | 16441 | 0 | 11 | 0 | 206 | 86 | 2 | 0 | 7 | 0 | 3 | 39 | 39 | 16 | 15985 | 768 | 115 | 0 | 120 | 119 | 56 | 33 | 252 | 48 |
| ShotgunWGS-ControlPig8GutMicrobiome-Day0 | 0 | 5661 | 0 | 416 | 2981 | 86 | 1373 | 1269 | 298 | 39909 | 20 | 126 | 344 | 375 | 539 | 554 | 1502 | 498 | 1045 | 2461 | 1433 | 281 | 354 | 68 | 1124 | 33 | 21 | 416 | 811 | 45 | 12 | 2182 | 633 | 333 | 624 | 149 | 1835 | 704 | 5891 | 337 | 19539 | 220 | 0 | 1 | 18 | 287 | 1036 | 186 | 1199 | 221 | 741 | 249 | 6175 | 2355 | 1943 | 5468 | 13087 | 48 | 1884 | 674 | 974 | 540 | 708 | 525 | 1501 | 18 | 126 | 1 | 0 | 145 | 327 | 42713 | 149 | 2 | 0 | 0 | 470 | 250 | 295 | 300 | 14 | 36494 | 442199 | 338 | 862 | 0 | 0 | 4 | 444 | 114 | 164 | 58 | 1 | 0 | 0 | 601 | 1 | 95976 | 1 | 5 | 514 | 166 | 23637 | 1 | 0 | 1069 | 509 | 40 | 14 | 427 | 6708 | 1036 | 2244 | 417 | 171 | 394 | 47 | 0 | 96 | 2327 | 3547 | 39907 | 204 | 0 | 9162 | 11870 | 450 | 53 | 66 | 8514 | 74 | 256 | 377 | 1730 | 69 | 2 | 160 | 1952 | 57 | 0 | 97 | 899 | 18 | 79 | 254 | 284 | 70 | 24 | 1 | 1865 | 49 | 0 | 3838 | 5382 | 89 | 338 | 7907 | 369 | 272 | 953 | 0 | 413 | 4072 | 586 | 26 | 17 | 457 | 3454 | 128 | 18 | 150 | 218 | 6 | 0 | 1106 | 2794 | 1107 | 649 | 14 | 0 | 0 | 599 | 500 | 680 | 1 | 375 | 47 | 688 | 32 | 0 | 616 | 25 | 424056 | 55 | 0 | 0 | 1 | 20485 | 391 | 250 | 412 | 169 | 0 | 43 | 2230 | 26769 | 543 | 473 | 3838 | 228 | 0 | 877 | 240 | 470 | 5494 | 1 | 74 | 1257 | 9 | 18 | 383 | 14 | 2011 | 56 | 1 | 1 | 0 | 2596 | 35 | 927 | 588 | 3698 | 379 | 1387 | 349 | 956 | 0 | 56 | 436 | 981 | 20232 | 744 | 911 | 396 | 762 | 124 | 1047 | 13009 | 9335 | 416 | 395 | 35 | 773 | 699 | 1632 | 10396 | 320 | 569 | 1554 | 375 | 193 | 776 | 62948 | 0 | 2525 | 0 | 395 | 12983 | 103 | 56 | 0 | 678 | 62 | 4 | 11 | 42 | 67 | 1267 | 763 | 15015 | 76 | 0 | 9 | 897 | 230 | 42 | 375 | 436 | 451 | 4404 | 24857 | 252845 | 0 | 2864 | 124365 | 205 | 108 | 114 | 968 | 6963 | 1080 | 117 | 3329 | 7259 | 0 | 1 | 750 | 1431 | 0 | 39 | 10721 | 196 | 1 | 5115 | 243 | 179 | 340 | 8831 | 7012 | 275 | 59 | 174 | 2 | 676 | 221 | 274 | 247 | 3 | 0 | 2455 | 225 | 617 | 31 | 1566 | 0 | 567 | 61 | 1200 | 387 | 114 | 90 | 60 | 96 | 66 | 172 | 109 | 90 | 308 | 50 | 94 | 2722 | 156 | 0 | 3541 | 0 | 5937 | 0 | 181 | 349 | 658 | 1 | 117 | 726 | 45 | 18363 | 0 | 96 | 169 | 325 | 46 | 133 | 255 | 0 | 0 | 486 | 60 | 24 | 2995 | 1 | 149 | 3 | 223 | 202 | 371 | 475 | 374 | 162 | 121 | 625 | 69 | 936 | 72 | 291 | 302 | 644 | 346 | 0 | 242 | 240 | 0 | 1 | 127 | 42 | 33 | 0 | 153576 | 2761 | 104 | 0 | 193 | 595 | 1719 | 1775 | 680 | 404 | 128 | 272 | 0 | 1 | 809 | 1 | 418 | 3048 | 1911 | 74 | 5766 | 0 | 14 | 25 | 80 | 108 | 0 | 0 | 113 | 1562 | 0 | 601 | 91 | 773 | 826 | 35 | 0 | 387 | 1442 | 258 | 703 | 0 | 736 | 85 | 132 | 1023 | 25118 | 864 | 266 | 523 | 57 | 2802 | 696 | 221 | 564 | 1776 | 1748 | 528 | 155 | 232 | 454 | 176 | 350 | 401 | 2403 | 614 | 222 | 509 | 539 | 27 | 98 | 207 | 473 | 354 | 84 | 872 | 172 | 534 | 49 | 94 | 231 | 160 | 32 | 285 | 142 | 410 | 74 | 331 | 673 | 91970 | 10473 | 0 | 23 | 0 | 88 | 5010 | 111 | 80 | 4 | 992 | 2469 | 1700 | 910 | 0 | 15 | 112 | 326 | 81 | 5 | 2109 | 73 | 99 | 221 | 35 | 1301 | 0 | 63 | 181 | 0 | 75 | 97 | 503 | 459 | 178 | 765 | 643 | 54 | 349 | 307 | 462 | 851 | 276 | 83 | 1 | 952 | 391 | 30 | 135 | 159 | 243 | 2209 | 488 | 0 | 69 | 3 | 0 | 1108 | 102 | 30551 | 4 | 1555 | 7440 | 81 | 0 | 0 | 95 | 143 | 0 | 161 | 741 | 2 | 16 | 19 | 11386 | 8899 | 601 | 39080 | 47 | 0 | 7 | 338 | 0 | 71 | 1019 | 366 | 141 | 528 | 15 | 0 | 871 | 2258 | 5681 | 35 | 3491 | 1261 | 5463 | 62 | 2644 | 2008 | 43 | 276 | 995 | 26 | 83 | 54 | 0 | 173 | 8 | 0 | 0 | 1130 | 481 | 0 | 156 | 23 | 109 | 290 | 666 | 1 | 236 | 0 | 162 | 40 | 1645 | 1012 | 0 | 395 | 0 | 23 | 16492 | 7 | 4 | 820657 | 1108 | 1128 | 263 | 0 | 427 | 1 | 240 | 0 | 1015 | 3925 | 2424 | 74 | 754 | 328 | 705 | 0 | 1 | 3061 | 0 | 26 | 134 | 626 | 0 | 761 | 0 | 24 | 2 | 349 | 204 | 1 | 1049 | 795 | 991 | 151 | 3 | 983 | 1365 | 772 | 972 | 1 | 306 | 10 | 840 | 1135 | 72078 | 39 | 2141 | 567 | 106 | 243 | 397 | 0 | 1032 | 0 | 532 | 233438 | 0 | 42 | 11 | 204 | 108 | 842 | 638 | 18 | 53 | 667 | 550 | 1238 | 699 | 1 | 855 | 5 | 87 | 40 | 164 | 12 | 3363 | 68 | 33861 | 680 | 3781 | 510 | 4244 | 176 | 92 | 0 | 907 | 17333 | 168 | 566 | 856 | 1017 | 188 | 368 | 326 | 4106 | 7 | 1 | 3749 | 0 | 274 | 5633 | 58 | 178 | 421 | 3 | 206 | 1115 | 32512 | 2943 | 509 | 65446 | 102 | 212 | 206 | 584 | 432 | 343 | 417 | 4358 | 0 | 4236 | 637 | 0 | 1217 | 4845 | 1665 | 1431 | 0 | 85 | 1 | 35 | 27 | 1 | 249 | 414 | 19 | 56 | 98 | 29 | 243 | 19 | 1323 | 951 | 3534 | 8290 | 4124 | 739 | 580 | 338 | 814 | 130 | 489 | 121 | 388 | 323 | 300 | 7 | 2772 | 5000 | 1238 | 12 | 599 | 3069 | 812 | 445 | 448 | 358 | 133 | 480 | 41 | 5331 | 516 | 1577 | 7 | 68 | 90 | 280 | 121 | 147 | 27 | 1135 | 23 | 241 | 98 | 0 | 23 | 0 | 323 | 1 | 19797 | 508 | 303 | 24 | 3077 | 1561 | 166 | 30 | 152 | 212 | 0 | 15 | 200 | 646 | 319 | 1179 | 169 | 796 | 327 | 109 | 0 | 1352 | 1959 | 27 | 269 | 3 | 0 | 6 | 209 | 1414 | 0 | 141 | 44 | 821 | 2204 | 17 | 815 | 72 | 49 | 171 | 0 | 47 | 520 | 16819 | 355 | 329 | 28383 | 550 | 1064 | 881 | 725 | 0 | 739 | 21035 | 0 | 16 | 0 | 587 | 597 | 2 | 2 | 33 | 0 | 2 | 85 | 95 | 12 | 51452 | 1069 | 191 | 0 | 336 | 254 | 80 | 33 | 311 | 33 |
| ShotgunWGS-ControlPig3GutMicrobiome-Day14 | 153 | 4117 | 0 | 267 | 2071 | 60 | 1015 | 817 | 197 | 31994 | 25 | 81 | 243 | 222 | 309 | 301 | 1126 | 245 | 653 | 1798 | 698 | 181 | 737 | 18 | 762 | 26 | 12 | 289 | 443 | 13 | 11 | 1419 | 464 | 302 | 272 | 141 | 1369 | 486 | 2917 | 325 | 13386 | 173 | 0 | 0 | 5 | 195 | 607 | 112 | 836 | 195 | 485 | 147 | 4189 | 1512 | 1216 | 4240 | 8660 | 44 | 1416 | 469 | 484 | 407 | 569 | 384 | 778 | 14 | 89 | 0 | 1 | 111 | 213 | 19924 | 84 | 3 | 0 | 0 | 338 | 184 | 238 | 153 | 6 | 26183 | 294856 | 170 | 561 | 0 | 0 | 0 | 315 | 50 | 96 | 38 | 0 | 2 | 0 | 222 | 0 | 25359 | 0 | 1 | 285 | 90 | 18590 | 0 | 0 | 841 | 246 | 21 | 12 | 223 | 4840 | 659 | 1647 | 205 | 115 | 225 | 30 | 1 | 76 | 1531 | 2412 | 34373 | 104 | 0 | 6622 | 9318 | 278 | 21 | 37 | 4769 | 55 | 210 | 216 | 790 | 31 | 1 | 87 | 1519 | 39 | 0 | 52 | 694 | 10 | 80 | 150 | 183 | 65 | 11 | 2 | 1413 | 31 | 0 | 2428 | 4140 | 69 | 270 | 3721 | 208 | 246 | 804 | 0 | 203 | 2758 | 492 | 21 | 31 | 309 | 2284 | 75 | 0 | 73 | 163 | 3 | 1 | 689 | 1986 | 898 | 610 | 7 | 0 | 0 | 374 | 336 | 337 | 0 | 168 | 31 | 470 | 27 | 0 | 335 | 39 | 299010 | 18 | 0 | 0 | 0 | 20465 | 323 | 169 | 293 | 117 | 0 | 14 | 1264 | 24714 | 356 | 247 | 1818 | 145 | 0 | 435 | 188 | 322 | 3273 | 0 | 29 | 873 | 6 | 16 | 462 | 10 | 1563 | 47 | 0 | 1 | 0 | 1595 | 15 | 369 | 381 | 3124 | 313 | 966 | 246 | 760 | 0 | 28 | 299 | 604 | 14708 | 498 | 585 | 338 | 528 | 71 | 822 | 10274 | 5420 | 271 | 237 | 22 | 589 | 492 | 895 | 7351 | 244 | 440 | 1109 | 153 | 118 | 414 | 32261 | 2 | 1656 | 2 | 238 | 9588 | 59 | 36 | 0 | 452 | 39 | 4 | 2 | 14 | 53 | 113 | 478 | 13222 | 54 | 0 | 4 | 584 | 103 | 33 | 305 | 209 | 309 | 3066 | 19759 | 269071 | 0 | 2082 | 175626 | 142 | 80 | 59 | 794 | 4985 | 621 | 74 | 2373 | 4924 | 0 | 1 | 509 | 954 | 0 | 26 | 6742 | 161 | 2 | 1795 | 172 | 80 | 241 | 6468 | 5172 | 177 | 24 | 104 | 0 | 536 | 137 | 170 | 103 | 5 | 0 | 1371 | 176 | 656 | 25 | 1008 | 0 | 369 | 44 | 910 | 274 | 86 | 46 | 37 | 57 | 35 | 103 | 78 | 41 | 202 | 49 | 75 | 2050 | 107 | 0 | 2163 | 0 | 5150 | 0 | 206 | 270 | 522 | 0 | 98 | 571 | 27 | 12582 | 0 | 88 | 99 | 182 | 35 | 76 | 198 | 0 | 0 | 326 | 35 | 16 | 2107 | 1 | 96 | 2 | 117 | 143 | 296 | 243 | 211 | 105 | 83 | 270 | 44 | 600 | 35 | 155 | 167 | 372 | 185 | 1 | 125 | 122 | 1 | 1 | 61 | 14 | 16 | 0 | 163871 | 4344 | 49 | 0 | 160 | 372 | 1507 | 1139 | 484 | 187 | 61 | 154 | 0 | 1 | 531 | 3 | 269 | 2470 | 2143 | 61 | 4387 | 0 | 12 | 17 | 62 | 63 | 0 | 0 | 94 | 1202 | 0 | 520 | 65 | 531 | 494 | 20 | 0 | 270 | 763 | 151 | 470 | 0 | 463 | 60 | 112 | 542 | 27391 | 521 | 194 | 311 | 34 | 1931 | 418 | 146 | 445 | 1226 | 1154 | 430 | 103 | 117 | 321 | 121 | 247 | 268 | 1527 | 380 | 171 | 373 | 343 | 38 | 54 | 138 | 284 | 228 | 61 | 631 | 162 | 376 | 32 | 77 | 136 | 106 | 23 | 177 | 73 | 376 | 56 | 222 | 348 | 57206 | 1189 | 0 | 12 | 1 | 55 | 3974 | 79 | 40 | 0 | 441 | 1460 | 1180 | 576 | 0 | 2 | 37 | 192 | 58 | 4 | 1539 | 51 | 68 | 159 | 21 | 814 | 0 | 32 | 113 | 1 | 36 | 48 | 297 | 355 | 126 | 482 | 406 | 38 | 241 | 212 | 272 | 397 | 158 | 63 | 0 | 701 | 366 | 20 | 68 | 114 | 153 | 1575 | 220 | 0 | 52 | 7 | 0 | 876 | 74 | 13557 | 2 | 1166 | 7969 | 78 | 0 | 0 | 72 | 96 | 0 | 112 | 582 | 7 | 3 | 1 | 8001 | 5183 | 404 | 23919 | 22 | 0 | 3 | 203 | 0 | 40 | 369 | 258 | 114 | 274 | 30 | 0 | 609 | 1780 | 3350 | 21 | 2774 | 1015 | 4192 | 33 | 1488 | 1213 | 19 | 219 | 660 | 18 | 49 | 22 | 0 | 181 | 1 | 1 | 0 | 850 | 288 | 0 | 55 | 13 | 83 | 229 | 397 | 2 | 111 | 0 | 98 | 20 | 906 | 780 | 0 | 271 | 0 | 32 | 8026 | 5 | 1 | 890023 | 964 | 618 | 169 | 0 | 274 | 2 | 147 | 0 | 709 | 2616 | 2232 | 40 | 465 | 169 | 520 | 0 | 0 | 1543 | 0 | 9 | 119 | 418 | 0 | 576 | 1 | 20 | 2 | 210 | 95 | 0 | 684 | 577 | 650 | 88 | 7 | 579 | 910 | 592 | 631 | 0 | 223 | 7 | 453 | 803 | 83235 | 24 | 1635 | 348 | 62 | 126 | 341 | 0 | 697 | 0 | 377 | 131810 | 5 | 33 | 13 | 112 | 72 | 871 | 333 | 16 | 28 | 464 | 379 | 760 | 391 | 1 | 376 | 0 | 33 | 31 | 62 | 13 | 2214 | 42 | 37426 | 513 | 2671 | 415 | 3675 | 139 | 113 | 0 | 586 | 11188 | 122 | 423 | 579 | 515 | 108 | 317 | 249 | 2292 | 0 | 0 | 2461 | 0 | 141 | 4510 | 51 | 99 | 215 | 1 | 136 | 823 | 431655 | 1787 | 294 | 75804 | 76 | 111 | 141 | 500 | 353 | 258 | 266 | 3115 | 0 | 3911 | 496 | 0 | 750 | 3692 | 1165 | 910 | 0 | 36 | 0 | 20 | 9 | 0 | 153 | 247 | 24 | 32 | 73 | 13 | 162 | 30 | 964 | 633 | 2661 | 5809 | 2962 | 484 | 355 | 177 | 532 | 85 | 348 | 93 | 304 | 218 | 175 | 8 | 1960 | 3508 | 917 | 25 | 474 | 2175 | 514 | 269 | 324 | 250 | 114 | 285 | 14 | 3013 | 358 | 466 | 0 | 31 | 40 | 167 | 65 | 93 | 19 | 641 | 10 | 152 | 28 | 0 | 9 | 0 | 292 | 0 | 17258 | 319 | 140 | 13 | 2111 | 620 | 109 | 17 | 111 | 219 | 0 | 15 | 129 | 368 | 241 | 770 | 129 | 447 | 213 | 42 | 0 | 1067 | 1432 | 12 | 246 | 4 | 0 | 7 | 104 | 1054 | 0 | 81 | 29 | 577 | 818 | 5 | 672 | 41 | 21 | 63 | 0 | 37 | 319 | 11368 | 207 | 247 | 19120 | 311 | 604 | 592 | 345 | 0 | 517 | 16846 | 0 | 16 | 0 | 214 | 163 | 1 | 0 | 19 | 3 | 0 | 63 | 48 | 9 | 21377 | 1145 | 134 | 0 | 159 | 138 | 34 | 16 | 182 | 62 |
| ShotgunWGS-TomatoPig14GutMicrobiome-Day7 | 0 | 1576 | 1 | 131 | 1012 | 41 | 399 | 391 | 90 | 11378 | 7 | 27 | 100 | 102 | 135 | 113 | 335 | 195 | 442 | 1000 | 405 | 91 | 135 | 8 | 1263 | 10 | 15 | 263 | 230 | 13 | 6 | 578 | 156 | 183 | 104 | 35 | 555 | 391 | 1410 | 132 | 5297 | 110 | 0 | 0 | 24 | 197 | 242 | 55 | 278 | 52 | 225 | 88 | 1576 | 762 | 457 | 1944 | 3531 | 18 | 532 | 170 | 443 | 217 | 243 | 155 | 406 | 4 | 44 | 0 | 0 | 43 | 62 | 3501 | 38 | 0 | 0 | 0 | 159 | 67 | 79 | 108 | 3 | 10048 | 107303 | 79 | 331 | 0 | 0 | 2 | 148 | 50 | 37 | 61 | 0 | 0 | 0 | 203 | 1 | 5542 | 0 | 1 | 105 | 40 | 7102 | 0 | 0 | 298 | 213 | 29 | 4 | 171 | 1844 | 240 | 599 | 121 | 46 | 160 | 54 | 0 | 119 | 694 | 1041 | 10853 | 66 | 0 | 2529 | 3572 | 122 | 7 | 21 | 2070 | 21 | 84 | 84 | 352 | 32 | 0 | 36 | 529 | 43 | 0 | 36 | 240 | 9 | 32 | 97 | 59 | 21 | 34 | 6 | 425 | 11 | 1 | 846 | 1489 | 41 | 101 | 1376 | 104 | 84 | 225 | 1 | 129 | 1136 | 192 | 15 | 6 | 89 | 759 | 28 | 0 | 36 | 50 | 1 | 1 | 255 | 737 | 308 | 216 | 4 | 2 | 0 | 178 | 173 | 137 | 0 | 33 | 12 | 410 | 5 | 0 | 205 | 7 | 109452 | 9 | 0 | 0 | 6 | 3136 | 214 | 62 | 113 | 55 | 0 | 14 | 471 | 5653 | 142 | 82 | 3171 | 101 | 0 | 184 | 96 | 268 | 921 | 0 | 19 | 320 | 3 | 5 | 13 | 3 | 580 | 23 | 0 | 0 | 0 | 571 | 6 | 193 | 163 | 1136 | 156 | 371 | 81 | 258 | 0 | 24 | 135 | 286 | 5163 | 200 | 254 | 138 | 207 | 39 | 304 | 3462 | 3867 | 114 | 112 | 13 | 240 | 219 | 392 | 1396 | 110 | 289 | 424 | 94 | 59 | 217 | 8251 | 0 | 560 | 0 | 257 | 2816 | 38 | 23 | 0 | 247 | 28 | 5 | 6 | 13 | 42 | 48 | 448 | 4365 | 17 | 6 | 3 | 290 | 71 | 17 | 210 | 122 | 111 | 12096 | 7325 | 55035 | 1 | 693 | 47886 | 196 | 35 | 44 | 255 | 1750 | 305 | 34 | 1326 | 1745 | 2 | 0 | 285 | 330 | 0 | 11 | 2992 | 65 | 0 | 511 | 118 | 31 | 61 | 2475 | 1952 | 95 | 7 | 41 | 1 | 176 | 66 | 58 | 69 | 2 | 0 | 622 | 80 | 190 | 9 | 849 | 0 | 217 | 24 | 304 | 134 | 42 | 26 | 23 | 28 | 14 | 68 | 29 | 32 | 110 | 22 | 35 | 731 | 78 | 0 | 3519 | 0 | 1694 | 1 | 93 | 122 | 200 | 0 | 33 | 357 | 22 | 4418 | 0 | 45 | 57 | 75 | 18 | 25 | 68 | 0 | 3 | 301 | 29 | 12 | 713 | 1 | 52 | 0 | 76 | 59 | 138 | 136 | 158 | 80 | 29 | 226 | 26 | 610 | 19 | 140 | 103 | 183 | 106 | 0 | 83 | 90 | 0 | 0 | 60 | 10 | 8 | 0 | 104615 | 809 | 141 | 0 | 90 | 240 | 435 | 510 | 259 | 104 | 37 | 85 | 0 | 1 | 237 | 0 | 106 | 745 | 545 | 41 | 1651 | 0 | 18 | 6 | 25 | 36 | 0 | 0 | 24 | 466 | 0 | 206 | 29 | 211 | 225 | 16 | 0 | 187 | 405 | 67 | 298 | 0 | 278 | 36 | 36 | 235 | 6151 | 229 | 129 | 110 | 23 | 2746 | 273 | 87 | 179 | 537 | 481 | 153 | 54 | 81 | 122 | 79 | 120 | 124 | 765 | 196 | 90 | 166 | 361 | 23 | 58 | 64 | 98 | 107 | 44 | 234 | 48 | 182 | 28 | 22 | 83 | 68 | 10 | 70 | 27 | 116 | 28 | 84 | 153 | 7754 | 5070 | 0 | 5 | 0 | 46 | 1363 | 58 | 136 | 4 | 222 | 718 | 595 | 234 | 0 | 2 | 27 | 97 | 39 | 6 | 599 | 13 | 21 | 71 | 12 | 511 | 0 | 19 | 55 | 2 | 45 | 30 | 131 | 140 | 49 | 256 | 239 | 22 | 116 | 92 | 141 | 219 | 67 | 24 | 1 | 257 | 80 | 12 | 31 | 38 | 72 | 598 | 94 | 0 | 26 | 4 | 0 | 286 | 38 | 2330 | 1 | 295 | 3460 | 22 | 0 | 0 | 44 | 29 | 0 | 54 | 378 | 126 | 6 | 5 | 2964 | 1842 | 290 | 10529 | 19 | 0 | 1 | 78 | 0 | 9 | 77 | 108 | 51 | 305 | 7 | 0 | 369 | 615 | 1243 | 10 | 949 | 303 | 1301 | 12 | 900 | 614 | 17 | 68 | 305 | 13 | 32 | 16 | 0 | 53 | 1 | 0 | 0 | 686 | 290 | 0 | 38 | 8 | 50 | 54 | 151 | 4 | 127 | 0 | 57 | 6 | 316 | 210 | 0 | 100 | 0 | 9 | 3428 | 1 | 0 | 277930 | 276 | 501 | 54 | 0 | 292 | 0 | 159 | 0 | 652 | 1471 | 630 | 19 | 263 | 79 | 476 | 0 | 1 | 645 | 0 | 13 | 53 | 217 | 0 | 240 | 0 | 7 | 4 | 113 | 69 | 1 | 283 | 189 | 298 | 45 | 1 | 199 | 438 | 285 | 247 | 0 | 113 | 20 | 175 | 318 | 12198 | 4 | 557 | 189 | 22 | 54 | 145 | 0 | 259 | 0 | 147 | 58127 | 1 | 15 | 3 | 76 | 41 | 271 | 140 | 5 | 11 | 167 | 157 | 1022 | 198 | 0 | 81 | 0 | 20 | 11 | 47 | 6 | 823 | 18 | 11115 | 335 | 1912 | 1500 | 1089 | 63 | 54 | 0 | 223 | 3372 | 106 | 122 | 269 | 210 | 62 | 120 | 76 | 1179 | 0 | 0 | 809 | 0 | 73 | 3212 | 22 | 57 | 151 | 0 | 46 | 272 | 9055 | 785 | 139 | 25259 | 27 | 84 | 61 | 190 | 120 | 112 | 118 | 1170 | 2 | 851 | 164 | 0 | 335 | 1360 | 410 | 352 | 0 | 18 | 0 | 5 | 9 | 1 | 104 | 80 | 9 | 15 | 29 | 6 | 66 | 11 | 357 | 290 | 978 | 2398 | 1040 | 196 | 194 | 87 | 242 | 42 | 149 | 33 | 111 | 110 | 101 | 3 | 819 | 1484 | 357 | 5 | 158 | 805 | 235 | 163 | 116 | 146 | 64 | 670 | 8 | 2161 | 173 | 251 | 2 | 19 | 26 | 60 | 19 | 48 | 6 | 406 | 8 | 80 | 23 | 0 | 11 | 0 | 67 | 0 | 4735 | 84 | 45 | 7 | 1727 | 407 | 59 | 9 | 67 | 73 | 0 | 17 | 74 | 260 | 86 | 363 | 126 | 207 | 121 | 30 | 0 | 772 | 501 | 4 | 63 | 2 | 0 | 6 | 64 | 377 | 0 | 32 | 77 | 929 | 413 | 10 | 378 | 19 | 17 | 59 | 0 | 17 | 208 | 4424 | 93 | 112 | 5413 | 194 | 284 | 203 | 233 | 0 | 357 | 5529 | 0 | 13 | 0 | 83 | 60 | 1 | 1 | 3 | 0 | 1 | 41 | 80 | 5 | 11160 | 395 | 93 | 1 | 55 | 47 | 106 | 31 | 88 | 115 |
| ShotgunWGS-ControlPig5GutMicrobiome-Day7 | 14 | 3708 | 0 | 230 | 1991 | 64 | 876 | 787 | 277 | 29680 | 16 | 90 | 205 | 243 | 341 | 304 | 1189 | 445 | 896 | 1778 | 623 | 179 | 224 | 47 | 1684 | 27 | 31 | 383 | 494 | 30 | 19 | 1214 | 514 | 341 | 302 | 152 | 1170 | 647 | 3503 | 270 | 12270 | 205 | 0 | 0 | 7 | 334 | 632 | 105 | 815 | 168 | 448 | 139 | 3168 | 1475 | 1134 | 3828 | 8846 | 43 | 1142 | 438 | 523 | 438 | 460 | 416 | 735 | 5 | 88 | 0 | 0 | 87 | 125 | 12791 | 99 | 1 | 0 | 0 | 406 | 184 | 220 | 225 | 6 | 22326 | 225359 | 206 | 673 | 0 | 0 | 2 | 294 | 67 | 101 | 64 | 0 | 0 | 0 | 218 | 1 | 66063 | 0 | 2 | 300 | 96 | 16872 | 2 | 0 | 830 | 262 | 24 | 6 | 203 | 4201 | 669 | 1387 | 214 | 105 | 258 | 36 | 0 | 202 | 1097 | 2653 | 28995 | 112 | 0 | 5764 | 8126 | 244 | 30 | 41 | 5906 | 36 | 308 | 231 | 808 | 57 | 1 | 83 | 1241 | 57 | 0 | 66 | 569 | 26 | 72 | 175 | 177 | 53 | 27 | 5 | 922 | 31 | 1 | 1881 | 3472 | 77 | 197 | 3799 | 193 | 178 | 546 | 0 | 191 | 2805 | 333 | 24 | 16 | 244 | 1743 | 86 | 4 | 107 | 113 | 4 | 0 | 656 | 1789 | 706 | 438 | 9 | 0 | 0 | 398 | 364 | 297 | 0 | 230 | 31 | 470 | 29 | 0 | 310 | 8 | 272328 | 31 | 0 | 0 | 0 | 10233 | 357 | 194 | 297 | 150 | 0 | 19 | 1189 | 18751 | 299 | 302 | 1826 | 152 | 0 | 433 | 150 | 340 | 2680 | 0 | 42 | 957 | 6 | 16 | 600 | 4 | 1285 | 41 | 0 | 0 | 0 | 1338 | 14 | 823 | 372 | 2490 | 226 | 825 | 329 | 550 | 0 | 35 | 279 | 608 | 13231 | 422 | 516 | 261 | 445 | 56 | 673 | 8336 | 7391 | 273 | 208 | 24 | 478 | 521 | 940 | 12959 | 244 | 480 | 897 | 154 | 122 | 367 | 32973 | 1 | 1235 | 0 | 416 | 7772 | 62 | 48 | 0 | 450 | 45 | 3 | 4 | 37 | 50 | 220 | 505 | 9131 | 76 | 0 | 6 | 539 | 130 | 39 | 297 | 223 | 296 | 1661 | 16498 | 181032 | 2 | 1686 | 114740 | 270 | 78 | 94 | 626 | 4040 | 672 | 49 | 2351 | 3360 | 0 | 1 | 530 | 890 | 0 | 36 | 6669 | 182 | 2 | 3518 | 173 | 102 | 211 | 5448 | 4373 | 164 | 36 | 76 | 0 | 372 | 133 | 153 | 115 | 0 | 0 | 1229 | 144 | 430 | 15 | 1322 | 0 | 462 | 35 | 747 | 277 | 86 | 82 | 42 | 55 | 39 | 132 | 51 | 80 | 211 | 40 | 69 | 1732 | 111 | 0 | 1893 | 1 | 4174 | 0 | 240 | 301 | 407 | 1 | 106 | 601 | 35 | 10907 | 0 | 68 | 100 | 146 | 41 | 64 | 188 | 0 | 0 | 475 | 42 | 24 | 1924 | 0 | 96 | 0 | 135 | 135 | 347 | 225 | 243 | 191 | 74 | 314 | 61 | 633 | 23 | 130 | 154 | 353 | 168 | 1 | 142 | 126 | 2 | 0 | 72 | 19 | 17 | 0 | 549401 | 1876 | 81 | 1 | 188 | 455 | 975 | 1002 | 548 | 173 | 63 | 174 | 0 | 2 | 485 | 0 | 297 | 1940 | 1203 | 95 | 3963 | 0 | 13 | 24 | 51 | 97 | 0 | 0 | 67 | 946 | 0 | 422 | 47 | 422 | 597 | 18 | 0 | 266 | 725 | 127 | 619 | 0 | 566 | 75 | 97 | 488 | 16036 | 481 | 172 | 330 | 50 | 1992 | 533 | 211 | 340 | 1187 | 1102 | 361 | 95 | 137 | 298 | 118 | 257 | 294 | 1574 | 339 | 143 | 371 | 397 | 25 | 81 | 130 | 370 | 302 | 73 | 615 | 141 | 399 | 60 | 105 | 196 | 147 | 13 | 120 | 68 | 256 | 57 | 216 | 317 | 50915 | 1108 | 0 | 7 | 0 | 56 | 3533 | 107 | 167 | 0 | 500 | 1488 | 1015 | 493 | 0 | 6 | 45 | 159 | 61 | 4 | 1383 | 55 | 75 | 151 | 18 | 918 | 0 | 33 | 95 | 0 | 90 | 49 | 296 | 316 | 116 | 503 | 465 | 31 | 295 | 193 | 207 | 413 | 162 | 62 | 0 | 613 | 290 | 20 | 76 | 111 | 143 | 1336 | 219 | 0 | 60 | 4 | 0 | 693 | 91 | 9197 | 2 | 858 | 4884 | 34 | 0 | 0 | 73 | 88 | 0 | 112 | 878 | 2 | 3 | 2 | 7065 | 3905 | 494 | 22016 | 30 | 0 | 6 | 226 | 0 | 39 | 694 | 228 | 100 | 545 | 28 | 1 | 690 | 1178 | 2743 | 34 | 2142 | 804 | 3400 | 28 | 1528 | 1306 | 36 | 172 | 604 | 16 | 47 | 30 | 0 | 103 | 3 | 1 | 0 | 1070 | 491 | 1 | 80 | 12 | 102 | 245 | 435 | 0 | 87 | 0 | 95 | 12 | 804 | 838 | 0 | 363 | 0 | 19 | 7825 | 6 | 2 | 507229 | 900 | 659 | 148 | 0 | 430 | 0 | 253 | 1 | 949 | 2830 | 1596 | 53 | 459 | 151 | 730 | 0 | 0 | 1687 | 0 | 9 | 99 | 427 | 1 | 704 | 0 | 21 | 2 | 211 | 114 | 3 | 650 | 672 | 617 | 82 | 3 | 592 | 1020 | 585 | 579 | 0 | 214 | 29 | 406 | 633 | 47030 | 34 | 1358 | 396 | 87 | 159 | 214 | 0 | 588 | 0 | 395 | 129723 | 4 | 39 | 2 | 112 | 71 | 575 | 351 | 8 | 34 | 379 | 346 | 837 | 346 | 0 | 570 | 1 | 46 | 26 | 59 | 15 | 2082 | 51 | 20968 | 575 | 3220 | 209 | 2773 | 178 | 68 | 0 | 606 | 9232 | 174 | 340 | 582 | 541 | 159 | 304 | 232 | 2377 | 3 | 0 | 1727 | 0 | 163 | 4225 | 54 | 110 | 266 | 2 | 139 | 645 | 41068 | 1825 | 269 | 43518 | 57 | 125 | 117 | 408 | 296 | 260 | 286 | 2779 | 0 | 4149 | 393 | 1 | 770 | 2980 | 979 | 907 | 0 | 42 | 1 | 7 | 21 | 0 | 171 | 184 | 6 | 24 | 59 | 19 | 255 | 14 | 840 | 612 | 2151 | 5295 | 2641 | 400 | 326 | 209 | 537 | 86 | 290 | 82 | 250 | 185 | 217 | 11 | 1748 | 2666 | 905 | 5 | 334 | 1910 | 481 | 348 | 324 | 280 | 111 | 922 | 22 | 3432 | 335 | 467 | 4 | 27 | 59 | 165 | 46 | 104 | 14 | 815 | 13 | 160 | 37 | 0 | 18 | 0 | 322 | 1 | 13193 | 336 | 182 | 4 | 2870 | 1165 | 99 | 22 | 99 | 119 | 0 | 23 | 145 | 296 | 253 | 780 | 193 | 359 | 296 | 52 | 0 | 1077 | 964 | 17 | 192 | 6 | 0 | 12 | 133 | 989 | 1 | 124 | 113 | 840 | 1026 | 30 | 600 | 43 | 22 | 102 | 0 | 26 | 369 | 10621 | 190 | 215 | 13467 | 387 | 493 | 425 | 349 | 0 | 607 | 15029 | 0 | 32 | 0 | 173 | 190 | 0 | 1 | 6 | 0 | 3 | 74 | 95 | 10 | 24441 | 718 | 129 | 0 | 197 | 139 | 122 | 41 | 200 | 13 |
| ShotgunWGS-TomatoPig18GutMicrobiome-Day7 | 1 | 1159 | 0 | 146 | 585 | 33 | 265 | 338 | 195 | 10604 | 6 | 85 | 139 | 135 | 171 | 261 | 1161 | 145 | 368 | 634 | 527 | 172 | 100 | 49 | 356 | 12 | 13 | 115 | 267 | 17 | 9 | 717 | 512 | 104 | 187 | 138 | 488 | 184 | 1636 | 139 | 4443 | 90 | 0 | 0 | 92 | 74 | 196 | 34 | 225 | 148 | 229 | 53 | 1402 | 450 | 593 | 1437 | 2878 | 27 | 467 | 183 | 244 | 148 | 194 | 183 | 713 | 2 | 37 | 0 | 0 | 79 | 104 | 15164 | 47 | 0 | 0 | 0 | 197 | 110 | 105 | 84 | 5 | 8839 | 168421 | 67 | 214 | 0 | 0 | 0 | 146 | 26 | 73 | 20 | 0 | 0 | 0 | 203 | 0 | 104397 | 0 | 0 | 176 | 45 | 7266 | 2 | 0 | 447 | 134 | 17 | 13 | 166 | 2073 | 462 | 502 | 216 | 102 | 134 | 49 | 0 | 32 | 378 | 1349 | 10996 | 58 | 1 | 2222 | 3243 | 109 | 14 | 11 | 1242 | 10 | 129 | 87 | 418 | 19 | 1 | 25 | 429 | 8 | 0 | 25 | 352 | 7 | 43 | 48 | 69 | 27 | 4 | 0 | 773 | 10 | 0 | 1136 | 1109 | 20 | 57 | 1120 | 178 | 58 | 374 | 0 | 201 | 751 | 223 | 11 | 4 | 170 | 1247 | 39 | 2 | 64 | 51 | 0 | 0 | 333 | 1052 | 372 | 250 | 16 | 0 | 0 | 173 | 132 | 203 | 0 | 168 | 13 | 459 | 19 | 0 | 397 | 15 | 101208 | 12 | 0 | 0 | 0 | 15652 | 149 | 183 | 150 | 67 | 0 | 12 | 389 | 5587 | 101 | 172 | 1853 | 73 | 0 | 204 | 70 | 189 | 1767 | 0 | 10 | 594 | 1 | 6 | 704 | 5 | 614 | 20 | 0 | 0 | 0 | 789 | 11 | 270 | 104 | 1041 | 230 | 443 | 248 | 232 | 0 | 37 | 106 | 209 | 4918 | 195 | 238 | 109 | 174 | 33 | 299 | 2951 | 1806 | 91 | 115 | 14 | 217 | 103 | 314 | 3230 | 101 | 198 | 423 | 62 | 80 | 220 | 8626 | 0 | 796 | 0 | 129 | 4429 | 30 | 20 | 0 | 155 | 28 | 0 | 1 | 6 | 14 | 31 | 352 | 4166 | 30 | 0 | 0 | 170 | 41 | 28 | 160 | 64 | 214 | 16141 | 8612 | 66371 | 0 | 677 | 136627 | 60 | 35 | 31 | 245 | 2071 | 169 | 40 | 881 | 2888 | 0 | 0 | 223 | 766 | 0 | 18 | 2416 | 66 | 5 | 4843 | 44 | 91 | 208 | 2135 | 2027 | 160 | 4 | 70 | 0 | 231 | 73 | 106 | 92 | 1 | 0 | 678 | 134 | 149 | 6 | 431 | 0 | 169 | 14 | 224 | 144 | 54 | 20 | 14 | 20 | 21 | 60 | 26 | 22 | 143 | 14 | 39 | 643 | 68 | 0 | 927 | 0 | 1360 | 0 | 173 | 142 | 244 | 0 | 40 | 183 | 15 | 4918 | 0 | 19 | 26 | 71 | 14 | 84 | 99 | 0 | 0 | 122 | 9 | 6 | 638 | 1 | 130 | 0 | 167 | 88 | 197 | 238 | 103 | 54 | 44 | 326 | 9 | 543 | 23 | 114 | 86 | 142 | 175 | 0 | 62 | 107 | 2 | 1 | 45 | 5 | 16 | 0 | 185934 | 932 | 255 | 0 | 74 | 181 | 612 | 835 | 237 | 191 | 31 | 51 | 0 | 0 | 235 | 2 | 335 | 796 | 670 | 72 | 1640 | 2 | 27 | 11 | 42 | 30 | 0 | 0 | 29 | 367 | 0 | 131 | 13 | 201 | 269 | 10 | 0 | 122 | 443 | 82 | 224 | 0 | 208 | 23 | 46 | 242 | 11524 | 250 | 66 | 264 | 20 | 691 | 176 | 70 | 159 | 427 | 485 | 171 | 39 | 37 | 115 | 56 | 122 | 95 | 508 | 171 | 69 | 141 | 147 | 19 | 18 | 72 | 412 | 129 | 50 | 441 | 129 | 222 | 15 | 125 | 72 | 71 | 10 | 89 | 41 | 145 | 26 | 151 | 154 | 8831 | 1015 | 0 | 6 | 0 | 24 | 1204 | 29 | 34 | 0 | 318 | 1337 | 396 | 327 | 0 | 0 | 28 | 158 | 38 | 1 | 508 | 19 | 14 | 42 | 9 | 317 | 0 | 14 | 57 | 2 | 19 | 17 | 112 | 209 | 59 | 229 | 261 | 15 | 112 | 81 | 177 | 491 | 122 | 23 | 1 | 347 | 238 | 13 | 54 | 83 | 79 | 493 | 138 | 0 | 22 | 0 | 0 | 344 | 62 | 11037 | 1 | 718 | 1696 | 13 | 0 | 0 | 39 | 36 | 0 | 52 | 154 | 120 | 2 | 15 | 2815 | 3156 | 176 | 15566 | 15 | 0 | 1 | 111 | 0 | 23 | 886 | 143 | 54 | 114 | 17 | 0 | 246 | 775 | 1779 | 30 | 906 | 503 | 1215 | 13 | 452 | 381 | 25 | 75 | 249 | 12 | 36 | 17 | 0 | 71 | 2 | 1 | 0 | 288 | 155 | 0 | 25 | 7 | 39 | 168 | 303 | 2 | 73 | 0 | 70 | 11 | 515 | 947 | 0 | 332 | 0 | 6 | 4741 | 4 | 0 | 478207 | 723 | 647 | 60 | 0 | 133 | 0 | 60 | 2 | 334 | 1457 | 585 | 15 | 177 | 93 | 177 | 0 | 0 | 559 | 0 | 7 | 42 | 179 | 0 | 380 | 0 | 11 | 2 | 73 | 111 | 1 | 364 | 457 | 492 | 57 | 0 | 358 | 537 | 272 | 315 | 0 | 128 | 4 | 183 | 365 | 15783 | 17 | 734 | 267 | 67 | 92 | 154 | 0 | 361 | 0 | 216 | 47208 | 1 | 9 | 7 | 82 | 116 | 419 | 362 | 2 | 19 | 197 | 330 | 1070 | 303 | 0 | 573 | 1 | 24 | 4 | 40 | 8 | 860 | 23 | 2400 | 247 | 1106 | 2014 | 941 | 84 | 64 | 0 | 354 | 4993 | 46 | 192 | 395 | 264 | 127 | 230 | 169 | 841 | 0 | 0 | 1487 | 0 | 101 | 3009 | 23 | 79 | 145 | 0 | 113 | 358 | 10184 | 1565 | 216 | 38373 | 45 | 53 | 43 | 253 | 100 | 70 | 75 | 1110 | 0 | 3488 | 206 | 0 | 304 | 1271 | 297 | 318 | 0 | 37 | 0 | 5 | 4 | 0 | 88 | 81 | 4 | 10 | 28 | 6 | 115 | 3 | 343 | 223 | 622 | 2078 | 913 | 198 | 302 | 160 | 171 | 31 | 162 | 41 | 141 | 151 | 88 | 4 | 577 | 674 | 312 | 10 | 189 | 714 | 219 | 162 | 151 | 84 | 89 | 130 | 10 | 1445 | 156 | 186 | 0 | 29 | 38 | 111 | 24 | 83 | 10 | 210 | 3 | 67 | 38 | 0 | 26 | 0 | 309 | 0 | 5017 | 314 | 151 | 3 | 962 | 237 | 72 | 6 | 48 | 68 | 0 | 11 | 64 | 125 | 154 | 485 | 67 | 328 | 106 | 18 | 0 | 547 | 736 | 12 | 108 | 6 | 0 | 5 | 139 | 314 | 0 | 53 | 10 | 1208 | 391 | 5 | 119 | 15 | 17 | 41 | 0 | 12 | 126 | 4323 | 84 | 75 | 3971 | 141 | 272 | 292 | 164 | 0 | 273 | 5333 | 0 | 6 | 0 | 105 | 100 | 0 | 1 | 19 | 0 | 2 | 32 | 25 | 8 | 12458 | 413 | 48 | 0 | 182 | 81 | 12 | 9 | 87 | 59 |
Calculate relative abundance, and bind back to metadata.
GenusOnly.Counts.Filt.t.wtotal <- GenusOnly.Counts.Filt.t %>%
mutate(Total.Counts = rowSums(GenusOnly.Counts.Filt.t[,2:ncol(GenusOnly.Counts.Filt.t)]))
dim(GenusOnly.Counts.Filt.t.wtotal)
## [1] 60 897
# create rel abund df
RelAbund.Genus.Filt <- GenusOnly.Counts.Filt.t.wtotal[,2:896]/GenusOnly.Counts.Filt.t.wtotal$Total.Counts
# add back metadata
RelAbund.Genus.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Genus.Filt)
The goal of these next bits of code are to understand how many missing values we have in our dataset, to set what parameters we will use for filtering.
# how many zeros are in the column AHJD-like viruses?
sum(RelAbund.Genus.Filt$`AHJD-like viruses` == 0) # this code works
## [1] 31
# remove metadata
# metadata is all character or factor, so can select only numeric columns
RelAbund.Genus.Filt.nometadata <- RelAbund.Genus.Filt %>%
select_if(is.numeric)
# create a list with the number of zeros for each genus
counting_zeros <- sapply(RelAbund.Genus.Filt.nometadata,
function(x){ (sum(x==0))})
# plot a histogram to look at missing values
counting_zeros_df <- as.data.frame(counting_zeros)
hist(counting_zeros_df$counting_zeros,
breaks = 61,
main = "Histogram of Genera with Zero Relative Intensity",
sub = "Starting at No Zeros",
xlab = "Number of zero relative intensity values",
ylab = "Frequency")
First column is no missing values, and its so big its hard to see how many missing values we actually have.
# plot a histogram to look, but removing genera that are only missing 1 value
counting_zeros_df_missingval <- counting_zeros_df %>%
rownames_to_column(var = "rowname") %>%
filter(counting_zeros > 0) %>%
column_to_rownames(var = "rowname")
# how many genera have at least one missing value?
dim(counting_zeros_df_missingval)
## [1] 186 1
186 genera have at least one missing value.
# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_df_missingval$counting_zeros,
breaks = 60,
main = "Histogram of Genera with Zero Relative Intensity",
sub = "Starting at 1 Zero",
xlab = "Number of zero relative intensity values",
ylab = "Frequency")
# plot a histogram to look, but removing genera that have 20 or more zeros
counting_zeros_df_missing20ormore <- counting_zeros_df %>%
rownames_to_column(var = "rowname") %>%
filter(counting_zeros >= 20) %>%
column_to_rownames(var = "rowname")
# histogram of number of zeros, starting at 20 zero
hist(counting_zeros_df_missing20ormore$counting_zeros,
breaks = 40,
main = "Histogram of Genera with Zero Relative Intensity",
sub = "Starting at 20 Zero",
xlab = "Number of zero relative intensity values",
ylab = "Frequency")
# how many genera have 20 or more missing value?
dim(counting_zeros_df_missing20ormore)
## [1] 140 1
There are 140 genera that have 20 or more missing values. Because 20 missing values here is 1/3 missing, we decided to use this as our cutoff.
Our decided criteria:
Filter out genera from relative abundance table that have > 20 zeros, or more than 33% missing data.
# make a character vector of genera names that have > 20 zeros from the rownames in above table
zeros.20 <- c(rownames(counting_zeros_df_missing20ormore))
# filter using this list
RelAbund.Genus.Filt.zerofilt <- RelAbund.Genus.Filt %>%
rownames_to_column(var = "rowname") %>%
select(everything(), -all_of(zeros.20)) %>%
column_to_rownames(var = "rowname")
RelAbund.Genus.Filt.zerofilt[1:3,1:6]
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## Diet_By_Time_Point Abiotrophia
## 1 Control Day 14 0.001305713
## 2 Control Day 0 0.001347804
## 3 Control Day 14 0.001066255
dim(RelAbund.Genus.Filt.zerofilt)
## [1] 60 760
Our final dataset has 755 genera (because there are 5 columns of metadata).
Write final dataset genus rel abund to .csv this way we have it.
write_csv(RelAbund.Genus.Filt.zerofilt,
file = "Genus_RelAbund_Final_Filtered_WithMetadata.csv")
Wrangling to enable collection of some summary statistics about our microbiome profile, including how many genera belong to different domains, etc.
Grab names of final genera.
# contains inplausible genera removed, but not removed for zeroes
dim(Genus.Counts.Filt)
## [1] 895 66
Genus.Counts.Filt[1:5, 1:10]
## # A tibble: 5 × 10
## domain phylum class order family genus `ShotgunWGS-Co…` `ShotgunWGS-Co…`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Viruses unclassi… uncl… Caud… Podov… AHJD… 29 0
## 2 Bacteria Firmicut… Baci… Lact… Aeroc… Abio… 5067 5661
## 3 Eukaryota unclassi… uncl… uncl… uncla… Acan… 0 0
## 4 Bacteria Cyanobac… uncl… uncl… uncla… Acar… 271 416
## 5 Bacteria Firmicut… Clos… Clos… Rumin… Acet… 1988 2981
## # … with 2 more variables: `ShotgunWGS-ControlPig3GutMicrobiome-Day14` <dbl>,
## # `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>
# final filtered data
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## Diet_By_Time_Point Abiotrophia Acaryochloris Acetivibrio Acetobacter
## 1 Control Day 14 0.001305713 6.983388e-05 0.0005122869 1.700751e-05
## 2 Control Day 0 0.001347804 9.904370e-05 0.0007097339 2.047538e-05
## 3 Control Day 14 0.001066255 6.914992e-05 0.0005363651 1.553931e-05
## 4 Tomato Day 7 0.001311580 1.090209e-04 0.0008422076 3.412106e-05
## 5 Control Day 7 0.001207244 7.488298e-05 0.0006482261 2.083700e-05
## Acetohalobium
## 1 0.0002669664
## 2 0.0003268918
## 3 0.0002628733
## 4 0.0003320562
## 5 0.0002852065
# grab colnames which have all the final genera
final_genera <- colnames(RelAbund.Genus.Filt.zerofilt)
# remove metadata colnames
final_genera <- final_genera[6:760]
final_genera <- as.data.frame(final_genera)
# create a df with the final genera we want to keep for our analysis
final_genera <- final_genera %>%
rename(genus = final_genera)
Get back domain and inner_join() with final_genera list
# pull from full dataset the domain and genus columns
Genus.Counts.Filt.Domain.Genera <- Genus.Counts.Filt %>%
select(domain, genus)
Genus.Counts.Filt.Domain.Genera[1:10,]
## # A tibble: 10 × 2
## domain genus
## <chr> <chr>
## 1 Viruses AHJD-like viruses
## 2 Bacteria Abiotrophia
## 3 Eukaryota Acanthamoeba
## 4 Bacteria Acaryochloris
## 5 Bacteria Acetivibrio
## 6 Bacteria Acetobacter
## 7 Bacteria Acetohalobium
## 8 Bacteria Acholeplasma
## 9 Bacteria Achromobacter
## 10 Bacteria Acidaminococcus
# want to join Genus.Counts.Filt.Domain.Genera with final_genera
final_genera_withdomain <- inner_join(final_genera, Genus.Counts.Filt.Domain.Genera,
by = "genus")
final_genera_withdomain %>%
count()
## n
## 1 755
final_genera_withdomain %>%
group_by(domain) %>%
count()
## # A tibble: 5 × 2
## # Groups: domain [5]
## domain n
## <chr> <int>
## 1 Archaea 60
## 2 Bacteria 582
## 3 Eukaryota 89
## 4 other sequences 1
## 5 Viruses 23
We have 755 total genera. We have 60 genera from Archaea, 582 from Bacteria, 89 from Eukaryota, and 23 from Viruses.
What are the most prevalent genera in our pigs?
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## Diet_By_Time_Point Abiotrophia Acaryochloris Acetivibrio Acetobacter
## 1 Control Day 14 0.001305713 6.983388e-05 0.0005122869 1.700751e-05
## 2 Control Day 0 0.001347804 9.904370e-05 0.0007097339 2.047538e-05
## 3 Control Day 14 0.001066255 6.914992e-05 0.0005363651 1.553931e-05
## 4 Tomato Day 7 0.001311580 1.090209e-04 0.0008422076 3.412106e-05
## 5 Control Day 7 0.001207244 7.488298e-05 0.0006482261 2.083700e-05
## Acetohalobium
## 1 0.0002669664
## 2 0.0003268918
## 3 0.0002628733
## 4 0.0003320562
## 5 0.0002852065
genera_means <- RelAbund.Genus.Filt.zerofilt %>%
summarize_if(is.numeric, mean)
genera_means_t <- t(genera_means)
genera_means_t <- as.data.frame(genera_means_t)
genera_means_t <- genera_means_t %>%
rename(rel_abund_genera = V1) %>%
arrange(-rel_abund_genera)
head(genera_means_t)
## rel_abund_genera
## Prevotella 0.22231328
## Bacteroides 0.10347888
## Clostridium 0.08556113
## Lactobacillus 0.06777787
## Eubacterium 0.05164571
## Faecalibacterium 0.04480044
The most prevalent genera are Prevotella (22.23% average abundance), Bacteroides (10.34%), Clostridium (8.56%), Lactobacillus (6.78%) and Eubacterium (5.16%).
What is the standard deviation of genera with the highest relative abundance?
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## Diet_By_Time_Point Abiotrophia Acaryochloris Acetivibrio Acetobacter
## 1 Control Day 14 0.001305713 6.983388e-05 0.0005122869 1.700751e-05
## 2 Control Day 0 0.001347804 9.904370e-05 0.0007097339 2.047538e-05
## 3 Control Day 14 0.001066255 6.914992e-05 0.0005363651 1.553931e-05
## 4 Tomato Day 7 0.001311580 1.090209e-04 0.0008422076 3.412106e-05
## 5 Control Day 7 0.001207244 7.488298e-05 0.0006482261 2.083700e-05
## Acetohalobium
## 1 0.0002669664
## 2 0.0003268918
## 3 0.0002628733
## 4 0.0003320562
## 5 0.0002852065
genera_sd <- RelAbund.Genus.Filt.zerofilt %>%
summarize_if(is.numeric, sd)
genera_sd_t <- t(genera_sd)
genera_sd_t <- as.data.frame(genera_sd_t)
genera_sd_t <- genera_sd_t %>%
rename(sd_genera = V1) %>%
arrange(-sd_genera)
head(genera_sd_t)
## sd_genera
## Prevotella 0.05410113
## Lactobacillus 0.04690415
## Streptococcus 0.04033402
## Bacteroides 0.01912817
## Faecalibacterium 0.01902410
## Clostridium 0.01803255
The standard deviations of most prevalent genera are Prevotella (5.4%), Bacteroides (1.9%), Clostridium (1.8%), Lactobacillus (4.6%) and Eubacterium (1.0%).
This section uses a different package than the rest of the analysis; data and metadata need to be uploaded again and made into format friendly for package.
# tax table
TAX_tab <- Genus.AllSamples.Counts %>%
select(Domain = domain, Phylum = phylum,
Class = class, Order = order,
Family = family, Genus = genus)
tax_names <- colnames(TAX_tab)
head(TAX_tab)
## # A tibble: 6 × 6
## Domain Phylum Class Order Family Genus
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Viruses unclassified (derived from Viruses) unclassifi… Caud… Podov… AHJD…
## 2 Bacteria Firmicutes Bacilli Lact… Aeroc… Abio…
## 3 Eukaryota unclassified (derived from Eukaryota) unclassifi… uncl… uncla… Acan…
## 4 Bacteria Cyanobacteria unclassifi… uncl… uncla… Acar…
## 5 Bacteria Firmicutes Clostridia Clos… Rumin… Acet…
## 6 Bacteria Proteobacteria Alphaprote… Rhod… Aceto… Acet…
head(tax_names)
## [1] "Domain" "Phylum" "Class" "Order" "Family" "Genus"
# OTU table
OTU_tab <- Genus.AllSamples.Counts[, seq(7, 66)]
head(OTU_tab)
## # A tibble: 6 × 60
## `ShotgunWGS-ControlPig6Gu…` `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-To…`
## <dbl> <dbl> <dbl> <dbl>
## 1 29 0 153 0
## 2 5067 5661 4117 1576
## 3 0 0 0 1
## 4 271 416 267 131
## 5 1988 2981 2071 1012
## 6 66 86 60 41
## # … with 56 more variables: `ShotgunWGS-ControlPig5GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-TomatoPig18GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-TomatoPig16GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-ControlPig10GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-ControlPig2GutMicrobiome-Day0` <dbl>,
## # `ShotgunWGS-TomatoPig18GutMicrobiome-Day0` <dbl>,
## # `ShotgunWGS-ControlPig10GutMicrobiome-Day0` <dbl>, …
Since metadata is contained in column names, we will parse them from here.
raw_names <- colnames(OTU_tab)
names_table <- data.frame(Raw_names = raw_names)
First, the string will split by the middle hyphen.
names_table <- names_table %>%
separate(Raw_names, into = c("Shotgun", "Type", "Day")) %>%
select(-Shotgun)
Now, since the character GutMicrobiome is constant over all samples, it will be removed. In the same manner, the character Day will be removed.
names_table <- names_table %>%
mutate(Type = str_remove(string = Type, pattern = "GutMicrobiome") ) %>%
mutate(Type = str_remove(string = Type, pattern = "GutMicrobime") ) %>%
mutate(Day = str_remove(string = Day, pattern = "Day"))
head(names_table, 2)
## Type Day
## 1 ControlPig6 14
## 2 ControlPig8 0
Since Pig is in the middle of the sample type and the pig number, it will be used as separator character. And the final result is a tidy data.
names_table <- names_table %>%
separate(col = Type, into = c("Type", "Pig"), sep = "Pig") %>%
mutate(Type = factor(Type), Pig = factor(Pig), Day = as.integer(Day)) %>%
select(Type, Day, Pig)
head(names_table)
## Type Day Pig
## 1 Control 14 6
## 2 Control 0 8
## 3 Control 14 3
## 4 Tomato 7 14
## 5 Control 7 5
## 6 Tomato 7 18
Now that we have tidy data, it’s better to replace long names with shorter ones. New names will be created as Type_Pig_Day. We are also creating names that distinguish the 6 diet by time point groups.
tmp_names <- names_table %>%
mutate(Pig = paste0("P", Pig), Day = paste0("D", Day)) %>%
unite("Kronas", Type:Day) %>% unite("Sample", Kronas:Pig, remove = F) %>%
select(-Pig)
head(tmp_names)
## Sample Kronas
## 1 Control_D14_P6 Control_D14
## 2 Control_D0_P8 Control_D0
## 3 Control_D14_P3 Control_D14
## 4 Tomato_D7_P14 Tomato_D7
## 5 Control_D7_P5 Control_D7
## 6 Tomato_D7_P18 Tomato_D7
metadata <- bind_cols(names_table, tmp_names)
head(metadata)
## Type Day Pig Sample Kronas
## 1 Control 14 6 Control_D14_P6 Control_D14
## 2 Control 0 8 Control_D0_P8 Control_D0
## 3 Control 14 3 Control_D14_P3 Control_D14
## 4 Tomato 7 14 Tomato_D7_P14 Tomato_D7
## 5 Control 7 5 Control_D7_P5 Control_D7
## 6 Tomato 7 18 Tomato_D7_P18 Tomato_D7
Finally, in order to use this metadata with the OTU table, which is linked by names, the row names of the metadata and the column names in the OTU table must be the same.
rownames(names_table) <- tmp_names$Sample
colnames(OTU_tab) <- tmp_names$Sample
It’s time create a phyloseq object that will allow us to analyze this data easier.
rownames(metadata) <- metadata$Sample
gut_microbiome_raw <- phyloseq(otu_table(OTU_tab, taxa_are_rows = T),
tax_table(TAX_tab),
sample_data(metadata))
colnames(tax_table(gut_microbiome_raw) ) <- tax_names
We can see that data at genus level accounts with 1085 taxas in 60 samples. But, we had developed a filtering scheme to remove very low abundance and inconsistently detected taxa, so let’s merge this full list
gut_microbiome_raw
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1085 taxa and 60 samples ]
## sample_data() Sample Data: [ 60 samples by 5 sample variables ]
## tax_table() Taxonomy Table: [ 1085 taxa by 6 taxonomic ranks ]
We want to only include the taxa we ended up using in our final analysis. We have already created an df final_genera which contains only the genera used in our final analysis.
final_genera_forphyloseq <- final_genera$genus
# subset to include only final genera
gut_microbiome_clean <- subset_taxa(gut_microbiome_raw, Genus %in% final_genera_forphyloseq)
gut_microbiome_clean
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 755 taxa and 60 samples ]
## sample_data() Sample Data: [ 60 samples by 5 sample variables ]
## tax_table() Taxonomy Table: [ 755 taxa by 6 taxonomic ranks ]
The final phyloseq object has 871 taxonomic levels, in our cases, species since it is the lowest taxonomic levels that the sequences were annotated.
In order to check if we have the 45 phyla, we are gonna to count the unique phyla names in the dataset.
length(unique(tax_table(gut_microbiome_clean)[, "Genus"]))
## [1] 755
We got our 755 genera.
plot_rarefaction <- ranacapa::ggrare(gut_microbiome_clean, step = 60000,
color = 'Type', se = F, plot = F)
## rarefying sample Control_D14_P6
## rarefying sample Control_D0_P8
## rarefying sample Control_D14_P3
## rarefying sample Tomato_D7_P14
## rarefying sample Control_D7_P5
## rarefying sample Tomato_D7_P18
## rarefying sample Tomato_D7_P16
## rarefying sample Control_D7_P10
## rarefying sample Control_D0_P2
## rarefying sample Tomato_D0_P18
## rarefying sample Control_D0_P10
## rarefying sample Control_D0_P7
## rarefying sample Control_D14_P8
## rarefying sample Tomato_D0_P11
## rarefying sample Tomato_D0_P19
## rarefying sample Tomato_D14_P17
## rarefying sample Control_D14_P9
## rarefying sample Control_D14_P10
## rarefying sample Tomato_D7_P19
## rarefying sample Control_D14_P5
## rarefying sample Control_D7_P2
## rarefying sample Control_D7_P6
## rarefying sample Tomato_D0_P12
## rarefying sample Tomato_D0_P14
## rarefying sample Control_D14_P7
## rarefying sample Tomato_D14_P11
## rarefying sample Tomato_D0_P20
## rarefying sample Control_D0_P9
## rarefying sample Tomato_D7_P11
## rarefying sample Tomato_D7_P13
## rarefying sample Tomato_D0_P17
## rarefying sample Tomato_D14_P19
## rarefying sample Tomato_D0_P13
## rarefying sample Control_D14_P2
## rarefying sample Control_D7_P1
## rarefying sample Tomato_D7_P15
## rarefying sample Tomato_D0_P15
## rarefying sample Tomato_D7_P12
## rarefying sample Tomato_D14_P14
## rarefying sample Tomato_D14_P20
## rarefying sample Control_D0_P1
## rarefying sample Control_D14_P4
## rarefying sample Control_D0_P6
## rarefying sample Tomato_D0_P16
## rarefying sample Tomato_D14_P16
## rarefying sample Tomato_D14_P18
## rarefying sample Control_D7_P7
## rarefying sample Control_D7_P4
## rarefying sample Tomato_D14_P13
## rarefying sample Control_D7_P8
## rarefying sample Tomato_D14_P15
## rarefying sample Tomato_D14_P12
## rarefying sample Tomato_D7_P20
## rarefying sample Control_D14_P1
## rarefying sample Control_D0_P3
## rarefying sample Control_D0_P5
## rarefying sample Control_D0_P4
## rarefying sample Control_D7_P9
## rarefying sample Control_D7_P3
## rarefying sample Tomato_D7_P17
plot_rarefaction <- plot_rarefaction + theme_test() +
facet_wrap("Day", scales = "free_x", ncol = 1,
labeller = labeller(Day = c( `0` = "Day 0" ,
`7` = "Day 7",
`14`= "Day 14")) ) +
labs(color = "Diet",
title = "Rarefaction curves") +
scale_color_manual(values = c( "steelblue2", "tomato2"))
plot_rarefaction
The psadd package is able to create Krona plots with an phyloseq object. Two Kronas will be created, per sample and per category Day + Type. The Krona plots only include the final filtered taxa we used in our analysis.
# Write kronas per samples
plot_krona(physeq = gut_microbiome_clean,
output = "kronas/per_sample",
variable = "Sample")
# Write kronas per category (Sample type + Day) i.e. Tomato_D7
plot_krona(physeq = gut_microbiome_clean,
output = "kronas/per_category",
variable = "Kronas")
Use PERMANOVA to conduct statistical analysis of overall microbial profile differences among groups.
Test the overall effect of Diet, Time_Point and their interaction of the overall microbiome. ORIGINAL
# create factors
factors_time_diet_pig <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)
# create permutations
perm_time_diet_pig <- how(nperm = 9999)
setBlocks(perm_time_diet_pig) <- with(factors_time_diet_pig, Pig)
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
data = factors_time_diet_pig,
permutations = perm_time_diet_pig,
method = "bray")
AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: with(factors_time_diet_pig, Pig)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig, permutations = perm_time_diet_pig, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.05879 0.04954 3.4114 0.0001 ***
## Time_Point 2 0.16612 0.13999 4.8196 0.0001 ***
## Diet:Time_Point 2 0.03113 0.02623 0.9031 0.3831
## Residual 54 0.93061 0.78424
## Total 59 1.18665 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Comparison when you don’t filter out for missing values
AllData.Genus.Filt.permanova.no0filt <- adonis2(RelAbund.Genus.Filt[,-c(1:5)]~Diet*Time_Point,
data = factors_time_diet_pig,
permutations = perm_time_diet_pig,
method = "bray")
AllData.Genus.Filt.permanova.no0filt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: with(factors_time_diet_pig, Pig)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = RelAbund.Genus.Filt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig, permutations = perm_time_diet_pig, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.05880 0.04954 3.4114 0.0001 ***
## Time_Point 2 0.16616 0.14000 4.8200 0.0001 ***
## Diet:Time_Point 2 0.03113 0.02623 0.9031 0.3750
## Residual 54 0.93079 0.78423
## Total 59 1.18689 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Significance is the same whether you filter for missing data or not.
NEW
set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)
# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_time_diet_pig_genus$Pig,
type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
data = factors_time_diet_pig_genus,
permutations = perm_time_diet_pig_genus,
method = "bray",
by = "margin")
AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: factors_time_diet_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_genus, permutations = perm_time_diet_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Diet:Time_Point 2 0.03113 0.02623 0.9031 0.36
## Residual 54 0.93061 0.78424
## Total 59 1.18665 1.00000
Interaction not significant (p=.355) so remove from model
set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)
# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_time_diet_pig_genus$Pig, type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet+Time_Point,
data = factors_time_diet_pig_genus,
permutations = perm_time_diet_pig_genus,
method = "bray",
by = "margin")
AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: factors_time_diet_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet + Time_Point, data = factors_time_diet_pig_genus, permutations = perm_time_diet_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.05879 0.04954 3.4232 0.060 .
## Time_Point 2 0.16612 0.13999 4.8364 0.005 **
## Residual 56 0.96174 0.81047
## Total 59 1.18665 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diet not significant p=.060 but close Time significant p=.005
Test for homogeneity of multivariate dispersions
dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Diet)
permutest(mod)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.001513 0.0015128 1.0653 999 0.332
## Residuals 58 0.082364 0.0014201
dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Time)
permutest(mod)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.008867 0.0044335 2.6538 999 0.081 .
## Residuals 57 0.095227 0.0016707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MANOVA TRIAL
a <- do.call(rbind, lapply(RelAbund.Genus.Filt.zerofilt, as.data.frame))
dep_vars <- cbind(RelAbund.Genus.Filt.zerofilt[-c(1:5)])
fit <- manova(cbind(RelAbund.Genus.Filt.zerofilt$Abiotrophia,RelAbund.Genus.Filt.zerofilt$Acaryochloris)~Diet*Time_Point + (1|Pig), data=RelAbund.Genus.Filt.zerofilt)
tidy(fit)
Effect in control diet of time.
set.seed(2021)
# filter for only control
control.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Control")
# create factors
factors_control_genera <- droplevels(control.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))
# create permutations
perm_control_genera <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_genera$Pig, type="none",))
# run permanova
Control.ByTime.Genus.zerofilt <- adonis2(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_genera,
permutations = perm_control_genera,
method = "bray",
by = "margin")
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
Control.ByTime.Genus.zerofilt
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_genera$Pig, plot permutation: none
## Permutation: series constant permutation within each Plot
## Number of permutations: 2
##
## adonis2(formula = control.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_genera, permutations = perm_control_genera, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 2 0.13507 0.22578 3.937 0.3333
## Residual 27 0.46317 0.77422
## Total 29 0.59824 1.00000
Significant effect of time (p = 0.005) within control samples.
Now do pairwise comparisons to see where the significance is coming from
set.seed(2021)
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 14")
# create factors
factors_control_T1T2_pig_genus <- droplevels(control.T1T2.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T1T2_pig_genus$Pig,
type = "free"))
# run PERMANOVA
Control.T1T2.Genus.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T1T2_pig_genus,
permutations = perm_control_T1T2_pig_genus,
method = "bray",
by = "margin")
Control.T1T2.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T2_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T1T2.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T2_pig_genus, permutations = perm_control_T1T2_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.03492 0.08631 1.7003 0.03 *
## Residual 18 0.36971 0.91369
## Total 19 0.40464 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Significant p = .030
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 7")
# create factors
factors_control_T1T3_pig_genus <- droplevels(control.T1T3.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T1T3_pig_genus$Pig,
type = "free"))
# run PERMANOVA
Control.T1T3.Genus.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T1T3_pig_genus,
permutations = perm_control_T1T3_pig_genus,
method = "bray",
by = "margin")
Control.T1T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T1T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T3_pig_genus, permutations = perm_control_T1T3_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.09340 0.28463 7.1617 0.01 **
## Residual 18 0.23474 0.71537
## Total 19 0.32814 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Significant p = .010
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 0")
# create factors
factors_control_T2T3_pig_genus <- droplevels(control.T2T3.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T2T3_pig_genus$Pig,
type = "free"))
# run PERMANOVA
Control.T2T3.Genus.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T2T3_pig_genus,
permutations = perm_control_T2T3_pig_genus,
method = "bray",
by = "margin")
Control.T2T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T2T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T2T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T2T3_pig_genus, permutations = perm_control_T2T3_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.07429 0.18752 4.1544 0.015 *
## Residual 18 0.32189 0.81248
## Total 19 0.39618 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sig: p=.015
Effect of tomato diet over time.
set.seed(2021)
# filter for only tomato
tomato.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Tomato")
# create factors
factors_tomato_genera <- droplevels(tomato.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))
# create permutations
perm_tomato_genera <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_tomato_genera$Pig, type="none",))
# run permanova
Tomato.ByTime.Genus.zerofilt <- adonis2(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_tomato_genera,
permutations = perm_tomato_genera,
method = "bray",
by = "margin")
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
Tomato.ByTime.Genus.zerofilt
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_genera$Pig, plot permutation: none
## Permutation: series constant permutation within each Plot
## Number of permutations: 2
##
## adonis2(formula = tomato.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_genera, permutations = perm_tomato_genera, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 2 0.06217 0.11739 1.7955 0.3333
## Residual 27 0.46745 0.88261
## Total 29 0.52962 1.00000
Significant effect of time (p = 0.01) within tomato samples
Now do pairwise comparisons to see where the significance is coming from
set.seed(2021)
# filter data set for only samples at T1 and T2
tomato.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 14")
# create factors
factors_tomato_T1T2_pig_genus <- droplevels(tomato.T1T2.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_tomato_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_tomato_T1T2_pig_genus$Pig,
type = "free"))
# run PERMANOVA
tomato.T1T2.Genus.zerofilt.permanova <- adonis2(tomato.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_tomato_T1T2_pig_genus,
permutations = perm_tomato_T1T2_pig_genus,
method = "bray",
by = "margin")
tomato.T1T2.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T1T2_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = tomato.T1T2.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T1T2_pig_genus, permutations = perm_tomato_T1T2_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.02556 0.07483 1.4559 0.09 .
## Residual 18 0.31598 0.92517
## Total 19 0.34154 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Not significant .090
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 7")
# create factors
factors_tomato_T1T3_pig_genus <- droplevels(tomato.T1T3.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_tomato_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_tomato_T1T3_pig_genus$Pig,
type = "free"))
# run PERMANOVA
tomato.T1T3.Genus.zerofilt.permanova <- adonis2(tomato.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_tomato_T1T3_pig_genus,
permutations = perm_tomato_T1T3_pig_genus,
method = "bray",
by = "margin")
tomato.T1T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T1T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = tomato.T1T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T1T3_pig_genus, permutations = perm_tomato_T1T3_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.04969 0.14718 3.1064 0.15
## Residual 18 0.28793 0.85282
## Total 19 0.33762 1.00000
Significant p = .150
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
Time_Point != "Day 0")
# create factors
factors_tomato_T2T3_pig_genus <- droplevels(tomato.T2T3.RelAbund.Genus.Filt.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_tomato_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_tomato_T2T3_pig_genus$Pig,
type = "free"))
# run PERMANOVA
tomato.T2T3.Genus.zerofilt.permanova <- adonis2(tomato.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
data = factors_tomato_T2T3_pig_genus,
permutations = perm_tomato_T2T3_pig_genus,
method = "bray",
by = "margin")
tomato.T2T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T2T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = tomato.T2T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T2T3_pig_genus, permutations = perm_tomato_T2T3_pig_genus, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.01801 0.0516 0.9794 0.12
## Residual 18 0.33098 0.9484
## Total 19 0.34899 1.0000
p is non significant =.120
Effect of diet at day 0.
# filter for day 0 only
d0.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 0")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day0_genera <- d0.RelAbund.Genus.Filt.zerofilt %>%
select(Diet)
# create permutations
perm_day0_genera <- how(nperm = 9999)
# run PERMANOVA
d0.ByTime.Genus.zerofilt <- adonis2(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
data = factors_day0_genera,
permutations = perm_day0_genera,
method = "bray")
d0.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d0.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day0_genera, permutations = perm_day0_genera, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.020138 0.0676 1.3051 0.2462
## Residual 18 0.277747 0.9324
## Total 19 0.297885 1.0000
Non-significant effect of Diet (p = 0.2402) at day 0.
Effect of diet at day 7.
# filter for day 7 only
d7.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 7")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day7_genera <- d7.RelAbund.Genus.Filt.zerofilt %>%
select(Diet)
# create permutations
perm_day7_genera <- how(nperm = 9999)
# run PERMANOVA
d7.ByTime.Genus.zerofilt <- adonis2(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
data = factors_day7_genera,
permutations = perm_day7_genera,
method = "bray")
d7.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d7.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day7_genera, permutations = perm_day7_genera, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.02780 0.0638 1.2267 0.2762
## Residual 18 0.40795 0.9362
## Total 19 0.43575 1.0000
Non-significant effect of Diet (p = 0.2836) at day 7.
Effect of diet at day 14.
# filter for day 14 only
d14.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 14")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day14_genera <- d14.RelAbund.Genus.Filt.zerofilt %>%
select(Diet)
# create permutations
perm_day14_genera <- how(nperm = 9999)
# run PERMANOVA
d14.ByTime.Genus.zerofilt <- adonis2(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
data = factors_day14_genera,
permutations = perm_day14_genera,
method = "bray")
d14.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d14.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day14_genera, permutations = perm_day14_genera, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.041978 0.14631 3.085 0.0062 **
## Residual 18 0.244923 0.85369
## Total 19 0.286900 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Significant effect of Diet (p = 0.005) at day 14.
# calculate distances
genus.filt.dist.20zeros <- vegdist(RelAbund.Genus.Filt.zerofilt[6:ncol(RelAbund.Genus.Filt.zerofilt)],
method = "bray")
# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.genus.filt.20zeros <- cmdscale(genus.filt.dist.20zeros, k=2)
# make into data frame
scale.genus.filt.df.20zeros <- as.data.frame(cbind(scale.genus.filt.20zeros,
AllSamples.Metadata))
# do PCoA again, but get eigen values
scale.genus.filt.20zeros.eig <- cmdscale(genus.filt.dist.20zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
eigs.genus.filt.20zeros <- (100* ((scale.genus.filt.20zeros.eig$eig)/(sum(scale.genus.filt.20zeros.eig$eig))))
# round the converted eigenvalues
round.eigs.genus.20zeros <- round(eigs.genus.filt.20zeros, 3)
All samples, one PCoA
PCoA_genera_20zeros_allsamples <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_classic() +
theme(axis.text = element_text(color = "black"))+
labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"),
y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Genus Level")
PCoA_genera_20zeros_allsamples
ggsave("Figures/BetaDiversity_PCoA_Genera_allsamples.png",
plot = PCoA_genera_20zeros_allsamples,
dpi = 800,
width = 10,
height = 8)
Re-level factors
scale.genus.filt.df.20zeros <- scale.genus.filt.df.20zeros %>%
mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
PCoA_genera_20zeros_facetbytime <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_bw() +
theme(axis.text = element_text(color = "black"),
strip.background =element_rect(fill="white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"),
y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Genera Level, Subset by Time Point") +
facet_wrap(~Time_Point)
PCoA_genera_20zeros_facetbytime
ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByTimePoint.png",
plot = PCoA_genera_20zeros_facetbytime,
dpi = 800,
width = 10,
height = 6)
PCoA_genera_20zeros_facetbydiet <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_bw() +
theme(axis.text = element_text(color = "black"),
strip.background =element_rect(fill="white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"),
y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Genera Level, Subset by Diet") +
facet_wrap(~Diet)
PCoA_genera_20zeros_facetbydiet
ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByDiet.png",
plot = PCoA_genera_20zeros_facetbydiet,
dpi = 800,
width = 10,
height = 6)
Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different and we didn’t feel this was the most accurate depction of the data.
# calculate distances
control.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)],
method = "bray")
# calculate to make PCoA
control.scale.genus.filt.20zeros <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2)
# filter metadata
meta.control <- subset(AllSamples.Metadata, Diet == "Control")
# make into data frame and add metadata
control.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.control, control.scale.genus.filt.20zeros))
# get eigenvalues
control.scale.genus.filt.20zeros.eig <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
control.eigs.genus.filt.20zeros <- (100*((control.scale.genus.filt.20zeros.eig$eig)/(sum(control.scale.genus.filt.20zeros.eig$eig))))
control.round.eigs.genus.20zeros <- round(control.eigs.genus.filt.20zeros, 3)
Reset factor levels
control.scale.genus.filt.20zeros.df$Time_Point <- factor(control.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))
Plot
control.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4"))+
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=(paste(control.round.eigs.genus.20zeros[1], "%")),
y=(paste(control.round.eigs.genus.20zeros[2], "%")),
fill = "Time Point",
title = "Beta Diversity",
subtitle = "Genera Level, Control Samples Only")
# calculate distances
tomato.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
tomato.scale.genus.filt.20zeros <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2)
# filter metadata
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")
# make into data frame and add metadata
tomato.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.tomato, tomato.scale.genus.filt.20zeros))
# get eigenvalues
tomato.scale.genus.filt.20zeros.eig <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
tomato.eigs.genus.filt.20zeros <- (100*((tomato.scale.genus.filt.20zeros.eig$eig)/(sum(tomato.scale.genus.filt.20zeros.eig$eig))))
tomato.round.eigs.genus.20zeros <- round(tomato.eigs.genus.filt.20zeros, 3)
Reset factor levels
tomato.scale.genus.filt.20zeros.df$Time_Point <- factor(tomato.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))
Plot
tomato.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point))+
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values = c("sienna1","firebrick3","tomato4"))+
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=(paste(tomato.round.eigs.genus.20zeros[1], "%")),
y=(paste(tomato.round.eigs.genus.20zeros[2], "%")),
fill = "Time Point",
title = "Beta Diversity",
subtitle = "Genera Level, Tomato Samples Only")
# calculate distances
d0.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d0.scale.genus.filt.20zeros <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2)
# filter metadata
meta.day0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")
# make into data frame and add metadata
d0.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day0, d0.scale.genus.filt.20zeros))
# get eigenvalues
d0.scale.genus.filt.20zeros.eig <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d0.eigs.genus.filt.20zeros <- (100*((d0.scale.genus.filt.20zeros.eig$eig)/(sum(d0.scale.genus.filt.20zeros.eig$eig))))
d0.round.eigs.genus.20zeros <- round(d0.eigs.genus.filt.20zeros, 3)
Plot
d0.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values = c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=(paste(d0.round.eigs.genus.20zeros[1], "%")),
y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
title = "Beta Diversity",
subtitle = "Genera Level, Day 0 Only")
# calculate distances
d7.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d7.scale.genus.filt.20zeros <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2)
# filter metadata
meta.day7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")
# make into data frame and add metadata
d7.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day7, d7.scale.genus.filt.20zeros))
# get eigenvalues
d7.scale.genus.filt.20zeros.eig <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d7.eigs.genus.filt.20zeros <- (100*((d7.scale.genus.filt.20zeros.eig$eig)/(sum(d7.scale.genus.filt.20zeros.eig$eig))))
d7.round.eigs.genus.20zeros <- round(d7.eigs.genus.filt.20zeros, 3)
Plot
d7.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_color_manual(values = c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=(paste(d7.round.eigs.genus.20zeros[1], "%")),
y=(paste(d7.round.eigs.genus.20zeros[2], "%")),
title = "Beta Diversity",
subtitle = "Genera Level, Day 7 Only")
# calculate distances
d14.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d14.scale.genus.filt.20zeros <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2)
# filter metadata
meta.day14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")
# make into data frame and add metadata
d14.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day14, d14.scale.genus.filt.20zeros))
# get eigenvalues
d14.scale.genus.filt.20zeros.eig <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d14.eigs.genus.filt.20zeros <- (100*((d14.scale.genus.filt.20zeros.eig$eig)/(sum(d14.scale.genus.filt.20zeros.eig$eig))))
d14.round.eigs.genus.20zeros <- round(d14.eigs.genus.filt.20zeros, 3)
Plot
d14.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_color_manual(values = c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=(paste(d14.round.eigs.genus.20zeros[1], "%")),
y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
title = "Beta Diversity",
subtitle = "Genera Level, Day 14 Only")
kable(head(RelAbund.Genus.Filt.zerofilt))
| Sample_Name | Pig | Diet | Time_Point | Diet_By_Time_Point | Abiotrophia | Acaryochloris | Acetivibrio | Acetobacter | Acetohalobium | Acholeplasma | Achromobacter | Acidaminococcus | Acidilobus | Acidimicrobium | Acidiphilium | Acidithiobacillus | Acidobacterium | Acidothermus | Acidovorax | Aciduliprofundum | Acinetobacter | Actinobacillus | Actinomyces | Actinosynnema | Aerococcus | Aeromicrobium | Aeromonas | Aeropyrum | Afipia | Aggregatibacter | Agrobacterium | Ahrensia | Ajellomyces | Akkermansia | Albidiferax | Alcanivorax | Algoriphagus | Alicycliphilus | Alicyclobacillus | Aliivibrio | Alistipes | Alkalilimnicola | Alkaliphilus | Allochromatium | Alphatorquevirus | Alteromonas | Aminobacterium | Aminomonas | Ammonifex | Amycolatopsis | Anabaena | Anaerobaculum | Anaerococcus | Anaerofustis | Anaeromyxobacter | Anaerostipes | Anaerotruncus | Anaplasma | Anoxybacillus | Aquifex | Arcanobacterium | Archaeoglobus | Arcobacter | Aromatoleum | Arthrobacter | Arthroderma | Arthrospira | Aspergillus | Asticcacaulis | Atopobium | Aurantimonas | Azoarcus | Azorhizobium | Azospirillum | Azotobacter | Babesia | Bacillus | Bacteroides | Bartonella | Basfia | Bdellovibrio | Beggiatoa | Beijerinckia | Bermanella | Beutenbergia | Bifidobacterium | Blastopirellula | Blattabacterium | Blautia | Bordetella | Borrelia | Botryotinia | Bpp-1-like viruses | Brachybacterium | Brachyspira | Bradyrhizobium | Brevibacillus | Brevibacterium | Brevundimonas | Brucella | Brugia | Buchnera | Bulleidia | Burkholderia | Butyrivibrio | Caenorhabditis | Caldanaerobacter | Caldicellulosiruptor | Calditerrivibrio | Caldivirga | Caminibacter | Campylobacter | Candida | Candidatus Accumulibacter | Candidatus Amoebophilus | Candidatus Azobacteroides | Candidatus Blochmannia | Candidatus Cloacamonas | Candidatus Desulforudis | Candidatus Hamiltonella | Candidatus Korarchaeum | Candidatus Koribacter | Candidatus Liberibacter | Candidatus Pelagibacter | Candidatus Phytoplasma | Candidatus Protochlamydia | Candidatus Puniceispirillum | Candidatus Regiella | Candidatus Riesia | Candidatus Solibacter | Candidatus Sulcia | Capnocytophaga | Carboxydothermus | Cardiobacterium | Carnobacterium | Catenibacterium | Catenulispora | Catonella | Caulobacter | Cellulomonas | Cellulosilyticum | Cellvibrio | Cenarchaeum | Chaetomium | Chelativorans | Chitinophaga | Chlamydia | Chlamydomonas | Chlamydophila | Chlorella | Chlorobaculum | Chlorobium | Chloroflexus | Chloroherpeton | Chlorovirus | Chromobacterium | Chromohalobacter | Chryseobacterium | Chthoniobacter | Citreicella | Citrobacter | Citromicrobium | Clavibacter | Clavispora | Clostridium | Coccidioides | Collinsella | Colwellia | Comamonas | Conexibacter | Congregibacter | Coprinopsis | Coprobacillus | Coprococcus | Coprothermobacter | Coraliomargarita | Corynebacterium | Coxiella | Croceibacter | Crocosphaera | Cronobacter | Cryptobacterium | Cryptosporidium | Cupriavidus | Cyanidioschyzon | Cyanidium | Cyanobium | Cyanophora | Cyanothece | Cylindrospermopsis | Cytophaga | Debaryomyces | Dechloromonas | Deferribacter | Dehalococcoides | Dehalogenimonas | Deinococcus | Delftia | Denitrovibrio | Dermacoccus | Desulfarculus | Desulfatibacillum | Desulfitobacterium | Desulfobacterium | Desulfococcus | Desulfohalobium | Desulfomicrobium | Desulfonatronospira | Desulfotalea | Desulfotomaculum | Desulfovibrio | Desulfurispirillum | Desulfurivibrio | Desulfurococcus | Desulfuromonas | Dethiobacter | Dethiosulfovibrio | Dialister | Dichelobacter | Dickeya | Dictyoglomus | Dictyostelium | Dinoroseobacter | Dokdonia | Dorea | Dyadobacter | Edwardsiella | Eggerthella | Ehrlichia | Eikenella | Elusimicrobium | Emericella | Emiliania | Encephalitozoon | Endoriftia | Enhydrobacter | Entamoeba | Enterobacter | Enterococcus | Enterocytozoon | Epsilon15-like viruses | Epulopiscium | Eremococcus | Eremothecium | Erwinia | Erysipelothrix | Erythrobacter | Escherichia | Ethanoligenens | Eubacterium | Exiguobacterium | Faecalibacterium | Ferrimonas | Ferroglobus | Ferroplasma | Fervidobacterium | Fibrobacter | Filifactor | Filobasidiella | Finegoldia | Flavobacterium | Francisella | Frankia | Fulvimarina | Fusobacterium | Gallionella | Gammaretrovirus | Gardnerella | Gemella | Gemmata | Gemmatimonas | Geobacillus | Geobacter | Geodermatophilus | Giardia | Gibberella | Gloeobacter | Gluconacetobacter | Gluconobacter | Gordonia | Gracilaria | Gramella | Granulibacter | Granulicatella | Guillardia | Haemophilus | Hahella | Halalkalicoccus | Halanaerobium | Haliangium | Haloarcula | Halobacterium | Haloferax | Halogeometricum | Halomicrobium | Halomonas | Haloquadratum | Halorhabdus | Halorhodospira | Halorubrum | Haloterrigena | Halothermothrix | Halothiobacillus | Helicobacter | Heliobacterium | Herbaspirillum | Herminiimonas | Herpetosiphon | Hirschia | Histophilus | Hoeflea | Holdemania | Hydrogenivirga | Hydrogenobacter | Hydrogenobaculum | Hyperthermus | Hyphomicrobium | Hyphomonas | Idiomarina | Ignicoccus | Ignisphaera | Ilyobacter | Intrasporangium | Janibacter | Jannaschia | Janthinobacterium | Jonesia | Jonquetella | Kangiella | Ketogulonicigenium | Kineococcus | Kingella | Klebsiella | Kluyveromyces | Kocuria | Kordia | Kosmotoga | Kribbella | Ktedonobacter | Kytococcus | L5-like viruses | Labrenzia | Laccaria | Lachancea | Lactobacillus | Lactococcus | Lambda-like viruses | Laribacter | Lawsonia | Leadbetterella | Leeuwenhoekiella | Legionella | Leifsonia | Leishmania | Lentisphaera | Leptosira | Leptospira | Leptospirillum | Leptothrix | Leptotrichia | Leuconostoc | Limnobacter | Listeria | Loa | Lodderomyces | Loktanella | Lutiella | Lyngbya | Lysinibacillus | Macrococcus | Magnaporthe | Magnetococcus | Magnetospirillum | Malassezia | Mannheimia | Maribacter | Maricaulis | Marinobacter | Marinomonas | Mariprofundus | Maritimibacter | Marivirga | Megasphaera | Meiothermus | Mesoplasma | Mesorhizobium | Metallosphaera | Methanobrevibacter | Methanocaldococcus | Methanocella | Methanococcoides | Methanococcus | Methanocorpusculum | Methanoculleus | Methanohalobium | Methanohalophilus | Methanoplanus | Methanopyrus | Methanoregula | Methanosaeta | Methanosarcina | Methanosphaera | Methanosphaerula | Methanospirillum | Methanothermobacter | Methanothermococcus | Methanothermus | Methylacidiphilum | Methylibium | Methylobacillus | Methylobacter | Methylobacterium | Methylocella | Methylococcus | Methylophaga | Methylosinus | Methylotenera | Methylovorus | Meyerozyma | Micrococcus | Microcoleus | Microcystis | Micromonas | Micromonospora | Microscilla | Mitsuokella | Mobiluncus | Moniliophthora | Monosiga | Moorella | Moraxella | Moritella | Mucilaginibacter | Mycobacterium | Mycoplasma | Myxococcus | N4-like viruses | Naegleria | Nakamurella | Nakaseomyces | Nanoarchaeum | Natranaerobius | Natrialba | Natronomonas | Nautilia | Nectria | Neisseria | Neorickettsia | Neosartorya | Neptuniibacter | Neurospora | Nitratiruptor | Nitrobacter | Nitrococcus | Nitrosococcus | Nitrosomonas | Nitrosopumilus | Nitrosospira | Nitrospira | Nocardia | Nocardioides | Nocardiopsis | Nodularia | Nostoc | Novosphingobium | Oceanibulbus | Oceanicaulis | Oceanicola | Oceanithermus | Oceanobacillus | Ochrobactrum | Octadecabacter | Odontella | Oenococcus | Oligotropha | Olsenella | Opitutus | Oribacterium | Orientia | Oscillatoria | Oscillochloris | Ostreococcus | Oxalobacter | P1-like viruses | P2-like viruses | P22-like viruses | Paenibacillus | Paludibacter | Pantoea | Parabacteroides | Parachlamydia | Paracoccidioides | Paracoccus | Paramecium | Parascardovia | Parvibaculum | Parvularcula | Pasteurella | Paulinella | Pectobacterium | Pediococcus | Pedobacter | Pelagibaca | Pelobacter | Pelodictyon | Pelotomaculum | Penicillium | Peptoniphilus | Peptostreptococcus | Perkinsus | Persephonella | Petrotoga | Phaeobacter | Phaeodactylum | Phaeosphaeria | Phenylobacterium | Phi29-like viruses | Photobacterium | Photorhabdus | Phytophthora | Pichia | Picrophilus | Pirellula | Planctomyces | Plasmodium | Plesiocystis | Podospora | Polaribacter | Polaromonas | Polynucleobacter | Porphyra | Porphyromonas | Postia | Prevotella | Prochlorococcus | Propionibacterium | Prosthecochloris | Proteus | Providencia | Pseudoalteromonas | Pseudomonas | Pseudoramibacter | Pseudovibrio | Psychrobacter | Psychroflexus | Psychromonas | Pyramidobacter | Pyrenophora | Pyrobaculum | Pyrococcus | Ralstonia | Raphidiopsis | Reclinomonas | Reinekea | Renibacterium | Rhizobium | Rhodobacter | Rhodococcus | Rhodomicrobium | Rhodomonas | Rhodopirellula | Rhodopseudomonas | Rhodospirillum | Rhodothermus | Rickettsia | Rickettsiella | Riemerella | Robiginitalea | Roseburia | Roseibium | Roseiflexus | Roseobacter | Roseomonas | Roseovarius | Rothia | Rubrobacter | Ruegeria | Ruminococcus | SP6-like viruses | SPO1-like viruses | SPbeta-like viruses | Saccharomonospora | Saccharomyces | Saccharophagus | Saccharopolyspora | Saccoglossus | Sagittula | Salinibacter | Salinispora | Salmonella | Sanguibacter | Scardovia | Scheffersomyces | Schizophyllum | Schizosaccharomyces | Sclerotinia | Sebaldella | Segniliparus | Selenomonas | Serratia | Shewanella | Shigella | Shuttleworthia | Sideroxydans | Simonsiella | Sinorhizobium | Slackia | Sodalis | Sorangium | Sphaerobacter | Sphingobacterium | Sphingobium | Sphingomonas | Sphingopyxis | Spirochaeta | Spirosoma | Stackebrandtia | Staphylococcus | Staphylothermus | Starkeya | Stenotrophomonas | Stigmatella | Streptobacillus | Streptococcus | Streptomyces | Streptosporangium | Subdoligranulum | Sulfitobacter | Sulfolobus | Sulfuricurvum | Sulfurihydrogenibium | Sulfurimonas | Sulfurospirillum | Sulfurovum | Symbiobacterium | Synechococcus | Synechocystis | Syntrophobacter | Syntrophomonas | Syntrophothermus | Syntrophus | T4-like viruses | T7-like viruses | Talaromyces | Teredinibacter | Terriglobus | Tetragenococcus | Tetrahymena | Thalassiosira | Thalassobium | Thauera | Theileria | Thermaerobacter | Thermanaerovibrio | Thermincola | Thermoanaerobacter | Thermoanaerobacterium | Thermobaculum | Thermobifida | Thermobispora | Thermococcus | Thermocrinis | Thermodesulfovibrio | Thermofilum | Thermomicrobium | Thermomonospora | Thermoplasma | Thermoproteus | Thermosediminibacter | Thermosinus | Thermosipho | Thermosphaera | Thermosynechococcus | Thermotoga | Thermus | Thioalkalivibrio | Thiobacillus | Thiomicrospira | Thiomonas | Tolumonas | Toxoplasma | Treponema | Trichodesmium | Trichomonas | Trichoplax | Tropheryma | Truepera | Trypanosoma | Tsukamurella | Tuber | Turicibacter | Uncinocarpus | Ureaplasma | Ustilago | Vanderwaltozyma | Variovorax | Veillonella | Verminephrobacter | Verrucomicrobium | Verticillium | Vibrio | Victivallis | Volvox | Vulcanisaeta | Waddlia | Weissella | Wigglesworthia | Wolbachia | Wolinella | Xanthobacter | Xanthomonas | Xenorhabdus | Xylanimonas | Xylella | Yarrowia | Yersinia | Zunongwangia | Zygosaccharomyces | Zymomonas | phiKZ-like viruses | unclassified (derived from Actinobacteria (class)) | unclassified (derived from Alicyclobacillaceae) | unclassified (derived from Alphaproteobacteria) | unclassified (derived from Alteromonadales) | unclassified (derived from Bacteria) | unclassified (derived from Bacteroidetes) | unclassified (derived from Betaproteobacteria) | unclassified (derived from Burkholderiales) | unclassified (derived from Campylobacterales) | unclassified (derived from Candidatus Poribacteria) | unclassified (derived from Caudovirales) | unclassified (derived from Chroococcales) | unclassified (derived from Clostridiales Family XI. Incertae Sedis) | unclassified (derived from Clostridiales) | unclassified (derived from Deltaproteobacteria) | unclassified (derived from Elusimicrobia) | unclassified (derived from Erysipelotrichaceae) | unclassified (derived from Euryarchaeota) | unclassified (derived from Flavobacteria) | unclassified (derived from Flavobacteriaceae) | unclassified (derived from Flavobacteriales) | unclassified (derived from Gammaproteobacteria) | unclassified (derived from Lachnospiraceae) | unclassified (derived from Methylophilales) | unclassified (derived from Myoviridae) | unclassified (derived from Opitutaceae) | unclassified (derived from Podoviridae) | unclassified (derived from Rhodobacteraceae) | unclassified (derived from Rhodobacterales) | unclassified (derived from Rickettsiales) | unclassified (derived from Ruminococcaceae) | unclassified (derived from Siphoviridae) | unclassified (derived from Thermotogales) | unclassified (derived from Verrucomicrobia subdivision 3) | unclassified (derived from Verrucomicrobiales) | unclassified (derived from Vibrionaceae) | unclassified (derived from Vibrionales) | unclassified (derived from Viruses) | unclassified (derived from other sequences) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShotgunWGS-ControlPig6GutMicrobiome-Day14 | 6 | Control | Day 14 | Control Day 14 | 0.0013057 | 6.98e-05 | 0.0005123 | 1.70e-05 | 0.0002670 | 0.0002007 | 0.0000495 | 0.0129311 | 2.6e-06 | 1.52e-05 | 6.29e-05 | 6.31e-05 | 0.0000941 | 0.0001002 | 0.0002036 | 0.0000559 | 0.0002046 | 0.0006754 | 0.0002587 | 4.97e-05 | 0.0000879 | 6.40e-06 | 0.0002193 | 8.0e-06 | 5.90e-06 | 0.0000948 | 0.0001332 | 4.10e-06 | 2.8e-06 | 0.0003798 | 0.0000881 | 0.0000657 | 0.0000441 | 2.09e-05 | 0.0003706 | 0.0001340 | 0.0005110 | 0.0001064 | 0.0033871 | 5.10e-05 | 2.10e-06 | 0.0000381 | 0.0001312 | 2.60e-05 | 0.0002358 | 4.15e-05 | 0.0001219 | 3.35e-05 | 0.0011854 | 0.0003992 | 0.0003378 | 0.0011052 | 0.0017118 | 5.70e-06 | 0.0003584 | 0.0001428 | 0.0001368 | 0.0001188 | 0.0001322 | 0.0001062 | 0.0002451 | 3.6e-06 | 2.37e-05 | 4.30e-05 | 7.40e-05 | 0.0091799 | 2.89e-05 | 0.0000737 | 4.90e-05 | 4.25e-05 | 4.95e-05 | 2.1e-06 | 0.0070241 | 0.0821852 | 5.72e-05 | 0.0001917 | 0.0000861 | 2.09e-05 | 2.47e-05 | 7.00e-06 | 0.0001046 | 0.0105635 | 0.0000760 | 1.34e-05 | 0.0045629 | 0.0002440 | 0.0000750 | 4.90e-06 | 1.19e-05 | 0.0000776 | 0.0013351 | 0.0001835 | 0.0004561 | 0.0000719 | 3.25e-05 | 0.0000711 | 9.00e-06 | 2.22e-05 | 0.0005981 | 0.0006347 | 0.0103885 | 3.48e-05 | 0.0017562 | 0.0022862 | 0.0000760 | 7.20e-06 | 8.50e-06 | 0.0011851 | 1.08e-05 | 0.0000523 | 5.05e-05 | 0.0001809 | 1.31e-05 | 1.62e-05 | 0.0003760 | 1.24e-05 | 1.93e-05 | 0.0001729 | 5.9e-06 | 1.57e-05 | 4.35e-05 | 4.56e-05 | 1.19e-05 | 4.60e-06 | 1.8e-06 | 0.0004154 | 3.60e-06 | 0.0005886 | 0.0011439 | 1.86e-05 | 5.98e-05 | 0.0012611 | 6.44e-05 | 4.56e-05 | 0.0002100 | 0.0000750 | 0.0006844 | 0.0001497 | 3.90e-06 | 7.7e-06 | 0.0000706 | 0.0005677 | 3.53e-05 | 3.27e-05 | 3.48e-05 | 0.0e+00 | 0.0001706 | 0.0006079 | 0.0002337 | 0.0001206 | 1.0e-06 | 0.0001036 | 0.0000902 | 0.0000845 | 2.19e-05 | 3.30e-06 | 0.0001175 | 4.10e-06 | 0.0001051 | 4.40e-06 | 0.0767302 | 3.10e-06 | 0.0049257 | 0.0000662 | 0.0000307 | 8.35e-05 | 3.68e-05 | 6.70e-06 | 0.0003621 | 0.0063405 | 0.0000866 | 0.0000523 | 0.0017554 | 5.00e-05 | 0.0001224 | 6.31e-05 | 0.0000956 | 0.0010161 | 6.70e-06 | 0.0002183 | 2.1e-06 | 6.4e-06 | 0.0000031 | 1.3e-06 | 0.0004015 | 1.42e-05 | 0.0004213 | 8.2e-06 | 0.0000923 | 0.0001430 | 0.0009210 | 0.0000850 | 0.0002654 | 0.0000569 | 0.0001660 | 8.00e-06 | 0.0000796 | 0.0001523 | 0.0037569 | 0.0001355 | 0.0001580 | 0.0000680 | 0.0001482 | 1.80e-05 | 0.0001946 | 0.0029103 | 0.0012480 | 8.53e-05 | 5.82e-05 | 4.40e-06 | 0.0001660 | 0.0001098 | 0.0002556 | 0.0062201 | 8.50e-05 | 0.0001167 | 0.0003136 | 4.51e-05 | 3.50e-05 | 0.0000943 | 0.0052538 | 0.0004803 | 0.0000763 | 0.0027436 | 1.65e-05 | 7.20e-06 | 0.0000995 | 1.39e-05 | 0.0e+00 | 5.0e-07 | 3.30e-06 | 8.20e-06 | 0.0000255 | 0.0001585 | 0.0027601 | 1.21e-05 | 1.0e-06 | 0.0001392 | 2.99e-05 | 9.30e-06 | 0.0000727 | 0.0000740 | 0.0000858 | 0.0005095 | 0.0045129 | 0.0713607 | 0.0005494 | 0.0345703 | 0.0000340 | 3.25e-05 | 2.45e-05 | 0.0002023 | 0.0012287 | 0.0001531 | 1.88e-05 | 0.0007628 | 0.0012769 | 0.0001080 | 0.0002448 | 1.03e-05 | 0.0018682 | 3.38e-05 | 3.0e-07 | 0.0006563 | 4.02e-05 | 2.37e-05 | 0.0000544 | 0.0018128 | 0.0014026 | 5.33e-05 | 6.20e-06 | 2.47e-05 | 0.0001211 | 2.89e-05 | 6.36e-05 | 3.22e-05 | 5.0e-07 | 0.0003646 | 4.17e-05 | 0.0001232 | 7.2e-06 | 0.0003079 | 0.0001116 | 1.11e-05 | 0.0002358 | 0.0000729 | 2.24e-05 | 1.65e-05 | 1.34e-05 | 1.57e-05 | 1.00e-05 | 3.12e-05 | 1.52e-05 | 1.49e-05 | 5.82e-05 | 1.06e-05 | 1.96e-05 | 0.0005736 | 2.53e-05 | 0.0005641 | 0.0013552 | 3.79e-05 | 0.0000572 | 0.0001337 | 3.14e-05 | 0.0001221 | 8.00e-06 | 0.0034288 | 1.78e-05 | 3.66e-05 | 6.78e-05 | 1.11e-05 | 2.01e-05 | 4.30e-05 | 0.0000742 | 1.11e-05 | 6.7e-06 | 0.0006200 | 3.84e-05 | 2.86e-05 | 3.30e-05 | 0.0000580 | 0.0000861 | 0.0000698 | 2.81e-05 | 1.75e-05 | 0.0000925 | 8.80e-06 | 0.0002023 | 9.80e-06 | 0.0000673 | 2.96e-05 | 0.0000956 | 5.57e-05 | 3.61e-05 | 4.17e-05 | 3.0e-07 | 1.57e-05 | 3.3e-06 | 7.5e-06 | 0.0460339 | 0.0006994 | 0.0000167 | 3.74e-05 | 0.0000863 | 0.0003785 | 0.0003412 | 0.0001433 | 0.0000557 | 2.19e-05 | 2.63e-05 | 5e-07 | 0.0001330 | 3.0e-07 | 0.0000559 | 0.0006277 | 0.0004314 | 8.00e-06 | 0.0011256 | 2.10e-06 | 3.3e-06 | 1.08e-05 | 2.09e-05 | 1.86e-05 | 0.0002981 | 0.0001296 | 1.49e-05 | 0.0001492 | 0.0001649 | 6.70e-06 | 0.0000982 | 0.0002020 | 3.53e-05 | 0.0001242 | 0.0001456 | 1.21e-05 | 2.27e-05 | 0.0001381 | 0.0101970 | 0.0001415 | 0.0000500 | 0.0000794 | 1.11e-05 | 0.0004927 | 0.0001075 | 4.61e-05 | 0.0001031 | 0.0003092 | 0.0003340 | 0.0000987 | 3.30e-05 | 3.50e-05 | 0.0000711 | 2.71e-05 | 7.32e-05 | 0.0000693 | 0.0004033 | 0.0001286 | 5.49e-05 | 0.0001090 | 0.0000902 | 2.60e-06 | 1.16e-05 | 2.63e-05 | 0.0000466 | 5.26e-05 | 1.03e-05 | 0.0001515 | 5.10e-05 | 0.0000964 | 3.30e-06 | 6.20e-06 | 3.63e-05 | 4.46e-05 | 6.2e-06 | 5.67e-05 | 1.88e-05 | 8.86e-05 | 1.24e-05 | 5.64e-05 | 0.0000796 | 0.0274527 | 0.0015438 | 3.9e-06 | 1.47e-05 | 0.0011094 | 2.37e-05 | 0.0000129 | 0.0001693 | 0.0004409 | 0.0003685 | 0.0001484 | 5.0e-07 | 8.20e-06 | 5.67e-05 | 1.83e-05 | 1.0e-06 | 0.0004064 | 8.20e-06 | 1.60e-05 | 4.07e-05 | 6.2e-06 | 0.0002317 | 1.42e-05 | 3.99e-05 | 9.80e-06 | 2.47e-05 | 0.0001008 | 0.0000835 | 3.12e-05 | 0.0001214 | 0.0001090 | 1.06e-05 | 6.60e-05 | 4.92e-05 | 0.0000941 | 0.0001098 | 4.59e-05 | 1.78e-05 | 0.0001775 | 0.0000698 | 5.4e-06 | 2.53e-05 | 2.94e-05 | 4.07e-05 | 0.0004453 | 0.0000706 | 1.37e-05 | 2.1e-06 | 0.0002000 | 3.71e-05 | 0.0066190 | 0.0002461 | 0.0050074 | 1.16e-05 | 1.08e-05 | 2.04e-05 | 2.37e-05 | 0.0001325 | 0.0000000 | 3.0e-07 | 0.0e+00 | 0.0020159 | 0.0012415 | 0.0001355 | 0.0063554 | 8.20e-06 | 1.0e-06 | 8.68e-05 | 1.08e-05 | 0.0002180 | 6.62e-05 | 2.19e-05 | 0.0001057 | 1.3e-06 | 0.0001788 | 0.0004473 | 0.0008558 | 6.40e-06 | 0.0006842 | 0.0003234 | 0.0011539 | 1.29e-05 | 0.0004218 | 0.0003533 | 8.20e-06 | 5.72e-05 | 0.0001745 | 5.20e-06 | 9.30e-06 | 1.00e-05 | 4.84e-05 | 0.0e+00 | 0.0002054 | 0.0000863 | 1.62e-05 | 2.1e-06 | 1.47e-05 | 4.07e-05 | 0.0000966 | 0.0000356 | 3.04e-05 | 4.4e-06 | 0.0002322 | 0.0001095 | 0.0000495 | 5.4e-06 | 0.0022865 | 1.3e-06 | 0.2653352 | 0.0001701 | 0.0002247 | 3.74e-05 | 0.0000853 | 0.0000304 | 0.0001982 | 0.0007669 | 0.0014498 | 1.29e-05 | 0.0001301 | 4.90e-05 | 0.0001294 | 0.0004409 | 4.60e-06 | 2.50e-05 | 0.0001142 | 0.0001497 | 6.4e-06 | 1.5e-06 | 5.23e-05 | 3.63e-05 | 0.0002098 | 0.0001556 | 0.0001904 | 2.14e-05 | 1.0e-06 | 0.0001554 | 0.0002415 | 0.0001373 | 0.0001670 | 5.54e-05 | 2.30e-06 | 0.0001265 | 0.0001886 | 0.0240077 | 4.40e-06 | 0.0003873 | 0.0000938 | 9.80e-06 | 3.50e-05 | 0.0000856 | 0.0002105 | 0.0000768 | 0.0373864 | 1.3e-06 | 9.00e-06 | 2.8e-06 | 3.43e-05 | 1.39e-05 | 0.0001763 | 0.0001085 | 6.7e-06 | 7.70e-06 | 0.0001175 | 0.0001028 | 0.0001987 | 0.0001417 | 0.0001647 | 1.26e-05 | 8.8e-06 | 1.47e-05 | 3.6e-06 | 0.0006641 | 8.80e-06 | 0.0093160 | 0.0001337 | 0.0007009 | 0.0000611 | 0.0013000 | 5.44e-05 | 1.29e-05 | 0.0001417 | 0.0034399 | 2.60e-05 | 0.0001118 | 0.0001392 | 0.0001142 | 2.11e-05 | 7.47e-05 | 6.78e-05 | 0.0006087 | 0.0006646 | 4.59e-05 | 0.0011864 | 7.70e-06 | 2.53e-05 | 0.0000636 | 3.30e-05 | 0.0001755 | 0.0284657 | 0.0004780 | 0.0000657 | 0.0212210 | 1.24e-05 | 4.07e-05 | 3.61e-05 | 0.0001567 | 0.0000925 | 7.06e-05 | 5.98e-05 | 0.0009068 | 0.0005893 | 0.0001281 | 0.0002147 | 0.0009277 | 0.0003136 | 0.0002525 | 5.70e-06 | 0.0e+00 | 1.19e-05 | 4.43e-05 | 5.82e-05 | 2.8e-06 | 8.80e-06 | 1.57e-05 | 5.4e-06 | 4.25e-05 | 4.1e-06 | 0.0002438 | 0.0001904 | 0.0007352 | 0.0016657 | 0.0009148 | 0.0001428 | 0.0001144 | 5.75e-05 | 0.0001263 | 2.45e-05 | 0.0000925 | 2.19e-05 | 6.44e-05 | 5.10e-05 | 4.69e-05 | 1.5e-06 | 0.0004958 | 0.0006368 | 0.0002332 | 3.6e-06 | 0.0001193 | 0.0005569 | 0.0001469 | 0.0000801 | 0.0000760 | 0.0000613 | 2.55e-05 | 0.0000902 | 1.11e-05 | 0.0008166 | 0.0001051 | 0.0001180 | 1.75e-05 | 1.47e-05 | 4.48e-05 | 1.19e-05 | 3.61e-05 | 2.1e-06 | 0.0001995 | 2.3e-06 | 4.95e-05 | 1.24e-05 | 2.80e-06 | 0.0000472 | 0.0061611 | 0.0000701 | 1.67e-05 | 1.8e-06 | 0.0005584 | 0.0001708 | 2.53e-05 | 4.4e-06 | 1.83e-05 | 4.48e-05 | 2.60e-06 | 2.94e-05 | 0.0001108 | 6.11e-05 | 0.0001958 | 0.0000356 | 0.0001737 | 0.0000636 | 1.34e-05 | 0.0003090 | 0.0003956 | 2.3e-06 | 5.13e-05 | 1.0e-06 | 2.65e-05 | 0.0003023 | 1.44e-05 | 8.20e-06 | 0.0001495 | 0.0002438 | 2.8e-06 | 0.0000941 | 1.24e-05 | 6.70e-06 | 1.65e-05 | 8.50e-06 | 0.0001085 | 0.0032562 | 4.79e-05 | 6.34e-05 | 0.0045420 | 0.0000876 | 0.0001551 | 0.0001721 | 0.0000673 | 0.0001054 | 0.0042367 | 2.80e-06 | 0.0000531 | 0.0000222 | 1.80e-06 | 1.00e-05 | 1.00e-05 | 4.1e-06 | 0.0041192 | 0.0001979 | 2.96e-05 | 0.0000309 | 3.07e-05 | 1.44e-05 | 8.50e-06 | 6.49e-05 | 1.24e-05 |
| ShotgunWGS-ControlPig8GutMicrobiome-Day0 | 8 | Control | Day 0 | Control Day 0 | 0.0013478 | 9.90e-05 | 0.0007097 | 2.05e-05 | 0.0003269 | 0.0003021 | 0.0000709 | 0.0095018 | 4.8e-06 | 3.00e-05 | 8.19e-05 | 8.93e-05 | 0.0001283 | 0.0001319 | 0.0003576 | 0.0001186 | 0.0002488 | 0.0005859 | 0.0003412 | 6.69e-05 | 0.0000843 | 1.62e-05 | 0.0002676 | 7.9e-06 | 5.00e-06 | 0.0000990 | 0.0001931 | 1.07e-05 | 2.9e-06 | 0.0005195 | 0.0001507 | 0.0000793 | 0.0001486 | 3.55e-05 | 0.0004369 | 0.0001676 | 0.0014026 | 0.0000802 | 0.0046520 | 5.24e-05 | 4.30e-06 | 0.0000683 | 0.0002467 | 4.43e-05 | 0.0002855 | 5.26e-05 | 0.0001764 | 5.93e-05 | 0.0014702 | 0.0005607 | 0.0004626 | 0.0013019 | 0.0031158 | 1.14e-05 | 0.0004486 | 0.0001605 | 0.0002319 | 0.0001286 | 0.0001686 | 0.0001250 | 0.0003574 | 4.3e-06 | 3.00e-05 | 3.45e-05 | 7.79e-05 | 0.0101694 | 3.55e-05 | 0.0001119 | 5.95e-05 | 7.02e-05 | 7.14e-05 | 3.3e-06 | 0.0086887 | 0.1052813 | 8.05e-05 | 0.0002052 | 0.0001057 | 2.71e-05 | 3.90e-05 | 1.38e-05 | 0.0001431 | 0.0228505 | 0.0001224 | 3.95e-05 | 0.0056276 | 0.0002545 | 0.0001212 | 9.50e-06 | 3.30e-06 | 0.0001017 | 0.0015971 | 0.0002467 | 0.0005343 | 0.0000993 | 4.07e-05 | 0.0000938 | 1.12e-05 | 2.29e-05 | 0.0005540 | 0.0008445 | 0.0095013 | 4.86e-05 | 0.0021813 | 0.0028261 | 0.0001071 | 1.26e-05 | 1.57e-05 | 0.0020271 | 1.76e-05 | 0.0000609 | 8.98e-05 | 0.0004119 | 1.64e-05 | 3.81e-05 | 0.0004647 | 1.36e-05 | 2.31e-05 | 0.0002140 | 4.3e-06 | 1.88e-05 | 6.05e-05 | 6.76e-05 | 1.67e-05 | 5.70e-06 | 2.0e-07 | 0.0004440 | 1.17e-05 | 0.0009138 | 0.0012814 | 2.12e-05 | 8.05e-05 | 0.0018825 | 8.79e-05 | 6.48e-05 | 0.0002269 | 0.0000983 | 0.0009695 | 0.0001395 | 6.20e-06 | 4.0e-06 | 0.0001088 | 0.0008223 | 3.05e-05 | 3.57e-05 | 5.19e-05 | 1.4e-06 | 0.0002633 | 0.0006652 | 0.0002636 | 0.0001545 | 3.3e-06 | 0.0001426 | 0.0001190 | 0.0001619 | 8.93e-05 | 1.12e-05 | 0.0001638 | 7.60e-06 | 0.0001467 | 6.00e-06 | 0.1009617 | 1.31e-05 | 0.0048772 | 0.0000931 | 0.0000595 | 9.81e-05 | 4.02e-05 | 1.02e-05 | 0.0005309 | 0.0063733 | 0.0001293 | 0.0001126 | 0.0009138 | 5.43e-05 | 0.0002088 | 5.71e-05 | 0.0001119 | 0.0013080 | 1.76e-05 | 0.0002993 | 2.1e-06 | 4.3e-06 | 0.0000912 | 3.3e-06 | 0.0004788 | 1.33e-05 | 0.0006181 | 8.3e-06 | 0.0002207 | 0.0001400 | 0.0008804 | 0.0000902 | 0.0003302 | 0.0000831 | 0.0002276 | 1.33e-05 | 0.0001038 | 0.0002336 | 0.0048170 | 0.0001771 | 0.0002169 | 0.0000943 | 0.0001814 | 2.95e-05 | 0.0002493 | 0.0030973 | 0.0022225 | 9.90e-05 | 9.40e-05 | 8.30e-06 | 0.0001840 | 0.0001664 | 0.0003886 | 0.0024751 | 7.62e-05 | 0.0001355 | 0.0003700 | 8.93e-05 | 4.60e-05 | 0.0001848 | 0.0149870 | 0.0006012 | 0.0000940 | 0.0030911 | 2.45e-05 | 1.33e-05 | 0.0001614 | 1.48e-05 | 1.0e-06 | 2.6e-06 | 1.00e-05 | 1.60e-05 | 0.0003017 | 0.0001817 | 0.0035749 | 1.81e-05 | 2.1e-06 | 0.0002136 | 5.48e-05 | 1.00e-05 | 0.0000893 | 0.0001038 | 0.0001074 | 0.0010485 | 0.0059181 | 0.0601988 | 0.0006819 | 0.0296095 | 0.0000488 | 2.57e-05 | 2.71e-05 | 0.0002305 | 0.0016578 | 0.0002571 | 2.79e-05 | 0.0007926 | 0.0017283 | 0.0001786 | 0.0003407 | 9.30e-06 | 0.0025525 | 4.67e-05 | 2.0e-07 | 0.0012178 | 5.79e-05 | 4.26e-05 | 0.0000809 | 0.0021025 | 0.0016695 | 6.55e-05 | 1.40e-05 | 4.14e-05 | 0.0001609 | 5.26e-05 | 6.52e-05 | 5.88e-05 | 7.0e-07 | 0.0005845 | 5.36e-05 | 0.0001469 | 7.4e-06 | 0.0003728 | 0.0001350 | 1.45e-05 | 0.0002857 | 0.0000921 | 2.71e-05 | 2.14e-05 | 1.43e-05 | 2.29e-05 | 1.57e-05 | 4.10e-05 | 2.60e-05 | 2.14e-05 | 7.33e-05 | 1.19e-05 | 2.24e-05 | 0.0006481 | 3.71e-05 | 0.0008431 | 0.0014135 | 4.31e-05 | 0.0000831 | 0.0001567 | 2.79e-05 | 0.0001729 | 1.07e-05 | 0.0043720 | 2.29e-05 | 4.02e-05 | 7.74e-05 | 1.10e-05 | 3.17e-05 | 6.07e-05 | 0.0001157 | 1.43e-05 | 5.7e-06 | 0.0007131 | 3.55e-05 | 5.31e-05 | 4.81e-05 | 0.0000883 | 0.0001131 | 0.0000890 | 3.86e-05 | 2.88e-05 | 0.0001488 | 1.64e-05 | 0.0002228 | 1.71e-05 | 0.0000693 | 7.19e-05 | 0.0001533 | 8.24e-05 | 5.76e-05 | 5.71e-05 | 0.0e+00 | 3.02e-05 | 1.0e-05 | 7.9e-06 | 0.0365643 | 0.0006574 | 0.0000248 | 4.60e-05 | 0.0001417 | 0.0004093 | 0.0004226 | 0.0001619 | 0.0000962 | 3.05e-05 | 6.48e-05 | 2e-07 | 0.0001926 | 2.0e-07 | 0.0000995 | 0.0007257 | 0.0004550 | 1.76e-05 | 0.0013728 | 3.30e-06 | 6.0e-06 | 1.90e-05 | 2.57e-05 | 2.69e-05 | 0.0003719 | 0.0001431 | 2.17e-05 | 0.0001840 | 0.0001967 | 8.30e-06 | 0.0000921 | 0.0003433 | 6.14e-05 | 0.0001674 | 0.0001752 | 2.02e-05 | 3.14e-05 | 0.0002436 | 0.0059802 | 0.0002057 | 0.0000633 | 0.0001245 | 1.36e-05 | 0.0006671 | 0.0001657 | 5.26e-05 | 0.0001343 | 0.0004228 | 0.0004162 | 0.0001257 | 3.69e-05 | 5.52e-05 | 0.0001081 | 4.19e-05 | 8.33e-05 | 0.0000955 | 0.0005721 | 0.0001462 | 5.29e-05 | 0.0001212 | 0.0001283 | 6.40e-06 | 2.33e-05 | 4.93e-05 | 0.0001126 | 8.43e-05 | 2.00e-05 | 0.0002076 | 4.10e-05 | 0.0001271 | 1.17e-05 | 2.24e-05 | 5.50e-05 | 3.81e-05 | 7.6e-06 | 6.79e-05 | 3.38e-05 | 9.76e-05 | 1.76e-05 | 7.88e-05 | 0.0001602 | 0.0218968 | 0.0024935 | 5.5e-06 | 2.10e-05 | 0.0011928 | 2.64e-05 | 0.0000190 | 0.0002362 | 0.0005878 | 0.0004047 | 0.0002167 | 3.6e-06 | 2.67e-05 | 7.76e-05 | 1.93e-05 | 1.2e-06 | 0.0005021 | 1.74e-05 | 2.36e-05 | 5.26e-05 | 8.3e-06 | 0.0003097 | 1.50e-05 | 4.31e-05 | 1.79e-05 | 2.31e-05 | 0.0001198 | 0.0001093 | 4.24e-05 | 0.0001821 | 0.0001531 | 1.29e-05 | 8.31e-05 | 7.31e-05 | 0.0001100 | 0.0002026 | 6.57e-05 | 1.98e-05 | 0.0002267 | 0.0000931 | 7.1e-06 | 3.21e-05 | 3.79e-05 | 5.79e-05 | 0.0005259 | 0.0001162 | 1.64e-05 | 7.0e-07 | 0.0002638 | 2.43e-05 | 0.0072738 | 0.0003702 | 0.0017714 | 1.93e-05 | 2.26e-05 | 3.40e-05 | 3.83e-05 | 0.0001764 | 0.0000005 | 3.8e-06 | 4.5e-06 | 0.0027108 | 0.0021187 | 0.0001431 | 0.0093044 | 1.12e-05 | 1.7e-06 | 8.05e-05 | 1.69e-05 | 0.0002426 | 8.71e-05 | 3.36e-05 | 0.0001257 | 3.6e-06 | 0.0002074 | 0.0005376 | 0.0013526 | 8.30e-06 | 0.0008312 | 0.0003002 | 0.0013007 | 1.48e-05 | 0.0006295 | 0.0004781 | 1.02e-05 | 6.57e-05 | 0.0002369 | 6.20e-06 | 1.98e-05 | 1.29e-05 | 4.12e-05 | 1.9e-06 | 0.0002690 | 0.0001145 | 3.71e-05 | 5.5e-06 | 2.60e-05 | 6.90e-05 | 0.0001586 | 0.0000562 | 3.86e-05 | 9.5e-06 | 0.0003917 | 0.0002409 | 0.0000940 | 5.5e-06 | 0.0039265 | 1.7e-06 | 0.1953868 | 0.0002638 | 0.0002686 | 6.26e-05 | 0.0001017 | 0.0000571 | 0.0002417 | 0.0009345 | 0.0005771 | 1.76e-05 | 0.0001795 | 7.81e-05 | 0.0001679 | 0.0007288 | 6.20e-06 | 3.19e-05 | 0.0001490 | 0.0001812 | 5.7e-06 | 5.0e-07 | 8.31e-05 | 4.86e-05 | 0.0002498 | 0.0001893 | 0.0002359 | 3.60e-05 | 7.0e-07 | 0.0002340 | 0.0003250 | 0.0001838 | 0.0002314 | 7.29e-05 | 2.40e-06 | 0.0002000 | 0.0002702 | 0.0171608 | 9.30e-06 | 0.0005097 | 0.0001350 | 2.52e-05 | 5.79e-05 | 0.0000945 | 0.0002457 | 0.0001267 | 0.0555783 | 0.0e+00 | 1.00e-05 | 2.6e-06 | 4.86e-05 | 2.57e-05 | 0.0002005 | 0.0001519 | 4.3e-06 | 1.26e-05 | 0.0001588 | 0.0001309 | 0.0002948 | 0.0001664 | 0.0002036 | 2.07e-05 | 9.5e-06 | 3.90e-05 | 2.9e-06 | 0.0008007 | 1.62e-05 | 0.0080618 | 0.0001619 | 0.0009002 | 0.0001214 | 0.0010104 | 4.19e-05 | 2.19e-05 | 0.0002159 | 0.0041267 | 4.00e-05 | 0.0001348 | 0.0002038 | 0.0002421 | 4.48e-05 | 8.76e-05 | 7.76e-05 | 0.0009776 | 0.0008926 | 6.52e-05 | 0.0013411 | 1.38e-05 | 4.24e-05 | 0.0001002 | 4.90e-05 | 0.0002655 | 0.0077406 | 0.0007007 | 0.0001212 | 0.0155818 | 2.43e-05 | 5.05e-05 | 4.90e-05 | 0.0001390 | 0.0001029 | 8.17e-05 | 9.93e-05 | 0.0010376 | 0.0010085 | 0.0001517 | 0.0002898 | 0.0011535 | 0.0003964 | 0.0003407 | 2.02e-05 | 8.3e-06 | 6.40e-06 | 5.93e-05 | 9.86e-05 | 4.5e-06 | 1.33e-05 | 2.33e-05 | 6.9e-06 | 5.79e-05 | 4.5e-06 | 0.0003150 | 0.0002264 | 0.0008414 | 0.0019737 | 0.0009819 | 0.0001759 | 0.0001381 | 8.05e-05 | 0.0001938 | 3.10e-05 | 0.0001164 | 2.88e-05 | 9.24e-05 | 7.69e-05 | 7.14e-05 | 1.7e-06 | 0.0006600 | 0.0011904 | 0.0002948 | 2.9e-06 | 0.0001426 | 0.0007307 | 0.0001933 | 0.0001059 | 0.0001067 | 0.0000852 | 3.17e-05 | 0.0001143 | 9.80e-06 | 0.0012692 | 0.0001229 | 0.0003755 | 1.62e-05 | 2.14e-05 | 6.67e-05 | 2.88e-05 | 3.50e-05 | 6.4e-06 | 0.0002702 | 5.5e-06 | 5.74e-05 | 2.33e-05 | 5.50e-06 | 0.0000769 | 0.0047134 | 0.0001209 | 7.21e-05 | 5.7e-06 | 0.0007326 | 0.0003717 | 3.95e-05 | 7.1e-06 | 3.62e-05 | 5.05e-05 | 3.60e-06 | 4.76e-05 | 0.0001538 | 7.59e-05 | 0.0002807 | 0.0000402 | 0.0001895 | 0.0000779 | 2.60e-05 | 0.0003219 | 0.0004664 | 6.4e-06 | 6.40e-05 | 1.4e-06 | 4.98e-05 | 0.0003367 | 3.36e-05 | 1.05e-05 | 0.0001955 | 0.0005247 | 4.0e-06 | 0.0001940 | 1.71e-05 | 1.17e-05 | 4.07e-05 | 1.12e-05 | 0.0001238 | 0.0040044 | 8.45e-05 | 7.83e-05 | 0.0067576 | 0.0001309 | 0.0002533 | 0.0002098 | 0.0001726 | 0.0001759 | 0.0050081 | 3.80e-06 | 0.0001398 | 0.0001421 | 7.90e-06 | 2.02e-05 | 2.26e-05 | 2.9e-06 | 0.0122500 | 0.0002545 | 4.55e-05 | 0.0000800 | 6.05e-05 | 1.90e-05 | 7.90e-06 | 7.40e-05 | 7.90e-06 |
| ShotgunWGS-ControlPig3GutMicrobiome-Day14 | 3 | Control | Day 14 | Control Day 14 | 0.0010663 | 6.91e-05 | 0.0005364 | 1.55e-05 | 0.0002629 | 0.0002116 | 0.0000510 | 0.0082861 | 6.5e-06 | 2.10e-05 | 6.29e-05 | 5.75e-05 | 0.0000800 | 0.0000780 | 0.0002916 | 0.0000635 | 0.0001691 | 0.0004657 | 0.0001808 | 4.69e-05 | 0.0001909 | 4.70e-06 | 0.0001973 | 6.7e-06 | 3.10e-06 | 0.0000748 | 0.0001147 | 3.40e-06 | 2.8e-06 | 0.0003675 | 0.0001202 | 0.0000782 | 0.0000704 | 3.65e-05 | 0.0003546 | 0.0001259 | 0.0007555 | 0.0000842 | 0.0034668 | 4.48e-05 | 1.30e-06 | 0.0000505 | 0.0001572 | 2.90e-05 | 0.0002165 | 5.05e-05 | 0.0001256 | 3.81e-05 | 0.0010849 | 0.0003916 | 0.0003149 | 0.0010981 | 0.0022428 | 1.14e-05 | 0.0003667 | 0.0001215 | 0.0001254 | 0.0001054 | 0.0001474 | 0.0000995 | 0.0002015 | 3.6e-06 | 2.30e-05 | 2.87e-05 | 5.52e-05 | 0.0051601 | 2.18e-05 | 0.0000875 | 4.77e-05 | 6.16e-05 | 3.96e-05 | 1.6e-06 | 0.0067811 | 0.0763643 | 4.40e-05 | 0.0001453 | 0.0000816 | 1.29e-05 | 2.49e-05 | 9.80e-06 | 0.0000575 | 0.0065677 | 0.0000738 | 2.33e-05 | 0.0048146 | 0.0002178 | 0.0000637 | 5.40e-06 | 3.10e-06 | 0.0000578 | 0.0012535 | 0.0001707 | 0.0004266 | 0.0000531 | 2.98e-05 | 0.0000583 | 7.80e-06 | 1.97e-05 | 0.0003965 | 0.0006247 | 0.0089022 | 2.69e-05 | 0.0017150 | 0.0024133 | 0.0000720 | 5.40e-06 | 9.60e-06 | 0.0012351 | 1.42e-05 | 0.0000544 | 5.59e-05 | 0.0002046 | 8.00e-06 | 2.25e-05 | 0.0003934 | 1.01e-05 | 1.35e-05 | 0.0001797 | 2.6e-06 | 2.07e-05 | 3.88e-05 | 4.74e-05 | 1.68e-05 | 2.80e-06 | 5.0e-07 | 0.0003660 | 8.00e-06 | 0.0006288 | 0.0010722 | 1.79e-05 | 6.99e-05 | 0.0009637 | 5.39e-05 | 6.37e-05 | 0.0002082 | 0.0000526 | 0.0007143 | 0.0001274 | 5.40e-06 | 8.0e-06 | 0.0000800 | 0.0005915 | 1.94e-05 | 1.89e-05 | 4.22e-05 | 8.0e-07 | 0.0001784 | 0.0005144 | 0.0002326 | 0.0001580 | 1.8e-06 | 0.0000969 | 0.0000870 | 0.0000873 | 4.35e-05 | 8.00e-06 | 0.0001217 | 7.00e-06 | 0.0000868 | 1.01e-05 | 0.0774401 | 4.70e-06 | 0.0053002 | 0.0000837 | 0.0000438 | 7.59e-05 | 3.03e-05 | 3.60e-06 | 0.0003274 | 0.0064006 | 0.0000922 | 0.0000640 | 0.0004708 | 3.76e-05 | 0.0001127 | 4.87e-05 | 0.0000834 | 0.0008477 | 7.50e-06 | 0.0002261 | 1.6e-06 | 4.1e-06 | 0.0001197 | 2.6e-06 | 0.0004048 | 1.22e-05 | 0.0004131 | 3.9e-06 | 0.0000956 | 0.0000987 | 0.0008091 | 0.0000811 | 0.0002502 | 0.0000637 | 0.0001968 | 7.30e-06 | 0.0000774 | 0.0001564 | 0.0038092 | 0.0001290 | 0.0001515 | 0.0000875 | 0.0001367 | 1.84e-05 | 0.0002129 | 0.0026608 | 0.0014037 | 7.02e-05 | 6.14e-05 | 5.70e-06 | 0.0001525 | 0.0001274 | 0.0002318 | 0.0019038 | 6.32e-05 | 0.0001140 | 0.0002872 | 3.96e-05 | 3.06e-05 | 0.0001072 | 0.0083552 | 0.0004289 | 0.0000616 | 0.0024832 | 1.53e-05 | 9.30e-06 | 0.0001171 | 1.01e-05 | 1.0e-06 | 5.0e-07 | 3.60e-06 | 1.37e-05 | 0.0000293 | 0.0001238 | 0.0034243 | 1.40e-05 | 1.0e-06 | 0.0001512 | 2.67e-05 | 8.50e-06 | 0.0000790 | 0.0000541 | 0.0000800 | 0.0007941 | 0.0051174 | 0.0696863 | 0.0005392 | 0.0454851 | 0.0000368 | 2.07e-05 | 1.53e-05 | 0.0002056 | 0.0012911 | 0.0001608 | 1.92e-05 | 0.0006146 | 0.0012753 | 0.0001318 | 0.0002471 | 6.70e-06 | 0.0017461 | 4.17e-05 | 5.0e-07 | 0.0004649 | 4.45e-05 | 2.07e-05 | 0.0000624 | 0.0016751 | 0.0013395 | 4.58e-05 | 6.20e-06 | 2.69e-05 | 0.0001388 | 3.55e-05 | 4.40e-05 | 2.67e-05 | 1.3e-06 | 0.0003551 | 4.56e-05 | 0.0001699 | 6.5e-06 | 0.0002611 | 0.0000956 | 1.14e-05 | 0.0002357 | 0.0000710 | 2.23e-05 | 1.19e-05 | 9.60e-06 | 1.48e-05 | 9.10e-06 | 2.67e-05 | 2.02e-05 | 1.06e-05 | 5.23e-05 | 1.27e-05 | 1.94e-05 | 0.0005309 | 2.77e-05 | 0.0005602 | 0.0013338 | 5.34e-05 | 0.0000699 | 0.0001352 | 2.54e-05 | 0.0001479 | 7.00e-06 | 0.0032586 | 2.28e-05 | 2.56e-05 | 4.71e-05 | 9.10e-06 | 1.97e-05 | 5.13e-05 | 0.0000844 | 9.10e-06 | 4.1e-06 | 0.0005457 | 2.49e-05 | 3.03e-05 | 3.70e-05 | 0.0000767 | 0.0000629 | 0.0000546 | 2.72e-05 | 2.15e-05 | 0.0000699 | 1.14e-05 | 0.0001554 | 9.10e-06 | 0.0000401 | 4.33e-05 | 0.0000963 | 4.79e-05 | 3.24e-05 | 3.16e-05 | 3.0e-07 | 1.58e-05 | 3.6e-06 | 4.1e-06 | 0.0424407 | 0.0011250 | 0.0000127 | 4.14e-05 | 0.0000963 | 0.0003903 | 0.0002950 | 0.0001254 | 0.0000484 | 1.58e-05 | 3.99e-05 | 3e-07 | 0.0001375 | 8.0e-07 | 0.0000697 | 0.0006397 | 0.0005550 | 1.58e-05 | 0.0011362 | 3.10e-06 | 4.4e-06 | 1.61e-05 | 1.63e-05 | 2.43e-05 | 0.0003113 | 0.0001347 | 1.68e-05 | 0.0001375 | 0.0001279 | 5.20e-06 | 0.0000699 | 0.0001976 | 3.91e-05 | 0.0001217 | 0.0001199 | 1.55e-05 | 2.90e-05 | 0.0001404 | 0.0070940 | 0.0001349 | 0.0000502 | 0.0000805 | 8.80e-06 | 0.0005001 | 0.0001083 | 3.78e-05 | 0.0001152 | 0.0003175 | 0.0002989 | 0.0001114 | 2.67e-05 | 3.03e-05 | 0.0000831 | 3.13e-05 | 6.40e-05 | 0.0000694 | 0.0003955 | 0.0000984 | 4.43e-05 | 0.0000966 | 0.0000888 | 9.80e-06 | 1.40e-05 | 3.57e-05 | 0.0000736 | 5.90e-05 | 1.58e-05 | 0.0001634 | 4.20e-05 | 0.0000974 | 8.30e-06 | 1.99e-05 | 3.52e-05 | 2.75e-05 | 6.0e-06 | 4.58e-05 | 1.89e-05 | 9.74e-05 | 1.45e-05 | 5.75e-05 | 0.0000901 | 0.0148157 | 0.0003079 | 3.1e-06 | 1.42e-05 | 0.0010292 | 2.05e-05 | 0.0000104 | 0.0001142 | 0.0003781 | 0.0003056 | 0.0001492 | 5.0e-07 | 9.60e-06 | 4.97e-05 | 1.50e-05 | 1.0e-06 | 0.0003986 | 1.32e-05 | 1.76e-05 | 4.12e-05 | 5.4e-06 | 0.0002108 | 8.30e-06 | 2.93e-05 | 9.30e-06 | 1.24e-05 | 0.0000769 | 0.0000919 | 3.26e-05 | 0.0001248 | 0.0001051 | 9.80e-06 | 6.24e-05 | 5.49e-05 | 0.0000704 | 0.0001028 | 4.09e-05 | 1.63e-05 | 0.0001816 | 0.0000948 | 5.2e-06 | 1.76e-05 | 2.95e-05 | 3.96e-05 | 0.0004079 | 0.0000570 | 1.35e-05 | 1.8e-06 | 0.0002269 | 1.92e-05 | 0.0035111 | 0.0003020 | 0.0020639 | 2.02e-05 | 1.86e-05 | 2.49e-05 | 2.90e-05 | 0.0001507 | 0.0000018 | 8.0e-07 | 3.0e-07 | 0.0020722 | 0.0013423 | 0.0001046 | 0.0061947 | 5.70e-06 | 8.0e-07 | 5.26e-05 | 1.04e-05 | 0.0000956 | 6.68e-05 | 2.95e-05 | 0.0000710 | 7.8e-06 | 0.0001577 | 0.0004610 | 0.0008676 | 5.40e-06 | 0.0007184 | 0.0002629 | 0.0010857 | 8.50e-06 | 0.0003854 | 0.0003142 | 4.90e-06 | 5.67e-05 | 0.0001709 | 4.70e-06 | 1.27e-05 | 5.70e-06 | 4.69e-05 | 3.0e-07 | 0.0002201 | 0.0000746 | 1.42e-05 | 3.4e-06 | 2.15e-05 | 5.93e-05 | 0.0001028 | 0.0000287 | 2.54e-05 | 5.2e-06 | 0.0002346 | 0.0002020 | 0.0000702 | 8.3e-06 | 0.0020786 | 1.3e-06 | 0.2305057 | 0.0002497 | 0.0001601 | 4.38e-05 | 0.0000710 | 0.0000381 | 0.0001836 | 0.0006775 | 0.0005781 | 1.04e-05 | 0.0001204 | 4.38e-05 | 0.0001347 | 0.0003996 | 2.30e-06 | 3.08e-05 | 0.0001083 | 0.0001492 | 5.2e-06 | 5.0e-07 | 5.44e-05 | 2.46e-05 | 0.0001771 | 0.0001494 | 0.0001683 | 2.28e-05 | 1.8e-06 | 0.0001500 | 0.0002357 | 0.0001533 | 0.0001634 | 5.78e-05 | 1.80e-06 | 0.0001173 | 0.0002080 | 0.0215569 | 6.20e-06 | 0.0004234 | 0.0000901 | 1.61e-05 | 3.26e-05 | 0.0000883 | 0.0001805 | 0.0000976 | 0.0341373 | 1.3e-06 | 8.50e-06 | 3.4e-06 | 2.90e-05 | 1.86e-05 | 0.0002256 | 0.0000862 | 4.1e-06 | 7.30e-06 | 0.0001202 | 0.0000982 | 0.0001968 | 0.0001013 | 0.0000974 | 8.50e-06 | 8.0e-06 | 1.61e-05 | 3.4e-06 | 0.0005734 | 1.09e-05 | 0.0096929 | 0.0001329 | 0.0006918 | 0.0001075 | 0.0009518 | 3.60e-05 | 2.93e-05 | 0.0001518 | 0.0028976 | 3.16e-05 | 0.0001096 | 0.0001500 | 0.0001334 | 2.80e-05 | 8.21e-05 | 6.45e-05 | 0.0005936 | 0.0006374 | 3.65e-05 | 0.0011680 | 1.32e-05 | 2.56e-05 | 0.0000557 | 3.52e-05 | 0.0002131 | 0.1117937 | 0.0004628 | 0.0000761 | 0.0196324 | 1.97e-05 | 2.87e-05 | 3.65e-05 | 0.0001295 | 0.0000914 | 6.68e-05 | 6.89e-05 | 0.0008067 | 0.0010129 | 0.0001285 | 0.0001942 | 0.0009562 | 0.0003017 | 0.0002357 | 9.30e-06 | 5.2e-06 | 2.30e-06 | 3.96e-05 | 6.40e-05 | 6.2e-06 | 8.30e-06 | 1.89e-05 | 3.4e-06 | 4.20e-05 | 7.8e-06 | 0.0002497 | 0.0001639 | 0.0006892 | 0.0015045 | 0.0007671 | 0.0001254 | 0.0000919 | 4.58e-05 | 0.0001378 | 2.20e-05 | 0.0000901 | 2.41e-05 | 7.87e-05 | 5.65e-05 | 4.53e-05 | 2.1e-06 | 0.0005076 | 0.0009085 | 0.0002375 | 6.5e-06 | 0.0001228 | 0.0005633 | 0.0001331 | 0.0000697 | 0.0000839 | 0.0000647 | 2.95e-05 | 0.0000738 | 3.60e-06 | 0.0007803 | 0.0000927 | 0.0001207 | 8.00e-06 | 1.04e-05 | 4.33e-05 | 1.68e-05 | 2.41e-05 | 4.9e-06 | 0.0001660 | 2.6e-06 | 3.94e-05 | 7.30e-06 | 2.30e-06 | 0.0000756 | 0.0044696 | 0.0000826 | 3.63e-05 | 3.4e-06 | 0.0005467 | 0.0001606 | 2.82e-05 | 4.4e-06 | 2.87e-05 | 5.67e-05 | 3.90e-06 | 3.34e-05 | 0.0000953 | 6.24e-05 | 0.0001994 | 0.0000334 | 0.0001158 | 0.0000552 | 1.09e-05 | 0.0002763 | 0.0003709 | 3.1e-06 | 6.37e-05 | 1.8e-06 | 2.69e-05 | 0.0002730 | 2.10e-05 | 7.50e-06 | 0.0001494 | 0.0002119 | 1.3e-06 | 0.0001740 | 1.06e-05 | 5.40e-06 | 1.63e-05 | 9.60e-06 | 0.0000826 | 0.0029442 | 5.36e-05 | 6.40e-05 | 0.0049519 | 0.0000805 | 0.0001564 | 0.0001533 | 0.0000894 | 0.0001339 | 0.0043629 | 4.10e-06 | 0.0000554 | 0.0000422 | 4.90e-06 | 1.63e-05 | 1.24e-05 | 2.3e-06 | 0.0055364 | 0.0002965 | 3.47e-05 | 0.0000412 | 3.57e-05 | 8.80e-06 | 4.10e-06 | 4.71e-05 | 1.61e-05 |
| ShotgunWGS-TomatoPig14GutMicrobiome-Day7 | 14 | Tomato | Day 7 | Tomato Day 7 | 0.0013116 | 1.09e-04 | 0.0008422 | 3.41e-05 | 0.0003321 | 0.0003254 | 0.0000749 | 0.0094690 | 5.8e-06 | 2.25e-05 | 8.32e-05 | 8.49e-05 | 0.0001123 | 0.0000940 | 0.0002788 | 0.0001623 | 0.0003678 | 0.0008322 | 0.0003370 | 7.57e-05 | 0.0001123 | 6.70e-06 | 0.0010511 | 8.3e-06 | 1.25e-05 | 0.0002189 | 0.0001914 | 1.08e-05 | 5.0e-06 | 0.0004810 | 0.0001298 | 0.0001523 | 0.0000866 | 2.91e-05 | 0.0004619 | 0.0003254 | 0.0011734 | 0.0001099 | 0.0044083 | 9.15e-05 | 2.00e-05 | 0.0001639 | 0.0002014 | 4.58e-05 | 0.0002314 | 4.33e-05 | 0.0001872 | 7.32e-05 | 0.0013116 | 0.0006342 | 0.0003803 | 0.0016178 | 0.0029386 | 1.50e-05 | 0.0004427 | 0.0001415 | 0.0003687 | 0.0001806 | 0.0002022 | 0.0001290 | 0.0003379 | 3.3e-06 | 3.66e-05 | 3.58e-05 | 5.16e-05 | 0.0029136 | 3.16e-05 | 0.0001323 | 5.58e-05 | 6.57e-05 | 8.99e-05 | 2.5e-06 | 0.0083622 | 0.0892998 | 6.57e-05 | 0.0002755 | 0.0001232 | 4.16e-05 | 3.08e-05 | 5.08e-05 | 0.0001689 | 0.0046122 | 0.0000874 | 3.33e-05 | 0.0059104 | 0.0002480 | 0.0001773 | 2.41e-05 | 3.30e-06 | 0.0001423 | 0.0015346 | 0.0001997 | 0.0004985 | 0.0001007 | 3.83e-05 | 0.0001332 | 4.49e-05 | 9.90e-05 | 0.0005776 | 0.0008663 | 0.0090321 | 5.49e-05 | 0.0021047 | 0.0029727 | 0.0001015 | 5.80e-06 | 1.75e-05 | 0.0017227 | 1.75e-05 | 0.0000699 | 6.99e-05 | 0.0002929 | 2.66e-05 | 3.00e-05 | 0.0004402 | 3.58e-05 | 3.00e-05 | 0.0001997 | 7.5e-06 | 2.66e-05 | 8.07e-05 | 4.91e-05 | 1.75e-05 | 2.83e-05 | 5.0e-06 | 0.0003537 | 9.20e-06 | 0.0007041 | 0.0012392 | 3.41e-05 | 8.41e-05 | 0.0011451 | 8.66e-05 | 6.99e-05 | 0.0001872 | 0.0001074 | 0.0009454 | 0.0001598 | 1.25e-05 | 5.0e-06 | 0.0000741 | 0.0006317 | 2.33e-05 | 3.00e-05 | 4.16e-05 | 8.0e-07 | 0.0002122 | 0.0006133 | 0.0002563 | 0.0001798 | 3.3e-06 | 0.0001481 | 0.0001440 | 0.0001140 | 2.75e-05 | 1.00e-05 | 0.0003412 | 4.20e-06 | 0.0001706 | 5.80e-06 | 0.0910882 | 7.50e-06 | 0.0026098 | 0.0001781 | 0.0000516 | 9.40e-05 | 4.58e-05 | 1.17e-05 | 0.0003920 | 0.0047045 | 0.0001182 | 0.0000682 | 0.0026390 | 8.41e-05 | 0.0001531 | 7.99e-05 | 0.0002230 | 0.0007665 | 1.58e-05 | 0.0002663 | 2.5e-06 | 4.2e-06 | 0.0000108 | 2.5e-06 | 0.0004827 | 1.91e-05 | 0.0004752 | 5.0e-06 | 0.0001606 | 0.0001357 | 0.0009454 | 0.0001298 | 0.0003088 | 0.0000674 | 0.0002147 | 2.00e-05 | 0.0001123 | 0.0002380 | 0.0042968 | 0.0001664 | 0.0002114 | 0.0001148 | 0.0001723 | 3.25e-05 | 0.0002530 | 0.0028811 | 0.0032182 | 9.49e-05 | 9.32e-05 | 1.08e-05 | 0.0001997 | 0.0001823 | 0.0003262 | 0.0011618 | 9.15e-05 | 0.0002405 | 0.0003529 | 7.82e-05 | 4.91e-05 | 0.0001806 | 0.0068667 | 0.0004660 | 0.0002139 | 0.0023435 | 3.16e-05 | 1.91e-05 | 0.0002056 | 2.33e-05 | 4.2e-06 | 5.0e-06 | 1.08e-05 | 3.50e-05 | 0.0000399 | 0.0003728 | 0.0036326 | 1.41e-05 | 2.5e-06 | 0.0002413 | 5.91e-05 | 1.41e-05 | 0.0001748 | 0.0001015 | 0.0000924 | 0.0100665 | 0.0060960 | 0.0458013 | 0.0005767 | 0.0398517 | 0.0001631 | 2.91e-05 | 3.66e-05 | 0.0002122 | 0.0014564 | 0.0002538 | 2.83e-05 | 0.0011035 | 0.0014522 | 0.0002372 | 0.0002746 | 9.20e-06 | 0.0024900 | 5.41e-05 | 0.0e+00 | 0.0004253 | 9.82e-05 | 2.58e-05 | 0.0000508 | 0.0020597 | 0.0016245 | 7.91e-05 | 5.80e-06 | 3.41e-05 | 0.0001465 | 5.49e-05 | 4.83e-05 | 5.74e-05 | 1.7e-06 | 0.0005176 | 6.66e-05 | 0.0001581 | 7.5e-06 | 0.0007066 | 0.0001806 | 2.00e-05 | 0.0002530 | 0.0001115 | 3.50e-05 | 2.16e-05 | 1.91e-05 | 2.33e-05 | 1.17e-05 | 5.66e-05 | 2.41e-05 | 2.66e-05 | 9.15e-05 | 1.83e-05 | 2.91e-05 | 0.0006084 | 6.49e-05 | 0.0029286 | 0.0014098 | 7.74e-05 | 0.0001015 | 0.0001664 | 2.75e-05 | 0.0002971 | 1.83e-05 | 0.0036768 | 3.74e-05 | 4.74e-05 | 6.24e-05 | 1.50e-05 | 2.08e-05 | 5.66e-05 | 0.0002505 | 2.41e-05 | 1.0e-05 | 0.0005934 | 4.33e-05 | 6.32e-05 | 4.91e-05 | 0.0001148 | 0.0001132 | 0.0001315 | 6.66e-05 | 2.41e-05 | 0.0001881 | 2.16e-05 | 0.0005077 | 1.58e-05 | 0.0001165 | 8.57e-05 | 0.0001523 | 8.82e-05 | 6.91e-05 | 7.49e-05 | 0.0e+00 | 4.99e-05 | 8.3e-06 | 6.7e-06 | 0.0870628 | 0.0006733 | 0.0001173 | 7.49e-05 | 0.0001997 | 0.0003620 | 0.0004244 | 0.0002155 | 0.0000866 | 3.08e-05 | 7.07e-05 | 8e-07 | 0.0001972 | 0.0e+00 | 0.0000882 | 0.0006200 | 0.0004536 | 3.41e-05 | 0.0013740 | 1.50e-05 | 5.0e-06 | 2.08e-05 | 3.00e-05 | 2.00e-05 | 0.0003878 | 0.0001714 | 2.41e-05 | 0.0001756 | 0.0001872 | 1.33e-05 | 0.0001556 | 0.0003370 | 5.58e-05 | 0.0002480 | 0.0002314 | 3.00e-05 | 3.00e-05 | 0.0001956 | 0.0051190 | 0.0001906 | 0.0001074 | 0.0000915 | 1.91e-05 | 0.0022853 | 0.0002272 | 7.24e-05 | 0.0001490 | 0.0004469 | 0.0004003 | 0.0001273 | 4.49e-05 | 6.74e-05 | 0.0001015 | 6.57e-05 | 9.99e-05 | 0.0001032 | 0.0006366 | 0.0001631 | 7.49e-05 | 0.0001381 | 0.0003004 | 1.91e-05 | 4.83e-05 | 5.33e-05 | 0.0000816 | 8.90e-05 | 3.66e-05 | 0.0001947 | 3.99e-05 | 0.0001515 | 2.33e-05 | 1.83e-05 | 6.91e-05 | 5.66e-05 | 8.3e-06 | 5.83e-05 | 2.25e-05 | 9.65e-05 | 2.33e-05 | 6.99e-05 | 0.0001273 | 0.0064530 | 0.0042194 | 4.2e-06 | 3.83e-05 | 0.0011343 | 4.83e-05 | 0.0001132 | 0.0001848 | 0.0005975 | 0.0004952 | 0.0001947 | 1.7e-06 | 2.25e-05 | 8.07e-05 | 3.25e-05 | 5.0e-06 | 0.0004985 | 1.08e-05 | 1.75e-05 | 5.91e-05 | 1.0e-05 | 0.0004253 | 1.58e-05 | 4.58e-05 | 3.74e-05 | 2.50e-05 | 0.0001090 | 0.0001165 | 4.08e-05 | 0.0002130 | 0.0001989 | 1.83e-05 | 9.65e-05 | 7.66e-05 | 0.0001173 | 0.0001823 | 5.58e-05 | 2.00e-05 | 0.0002139 | 0.0000666 | 1.0e-05 | 2.58e-05 | 3.16e-05 | 5.99e-05 | 0.0004977 | 0.0000782 | 2.16e-05 | 3.3e-06 | 0.0002380 | 3.16e-05 | 0.0019391 | 0.0002455 | 0.0028795 | 1.83e-05 | 3.66e-05 | 2.41e-05 | 4.49e-05 | 0.0003146 | 0.0001049 | 5.0e-06 | 4.2e-06 | 0.0024667 | 0.0015330 | 0.0002413 | 0.0087625 | 1.58e-05 | 8.0e-07 | 6.49e-05 | 7.50e-06 | 0.0000641 | 8.99e-05 | 4.24e-05 | 0.0002538 | 5.8e-06 | 0.0003071 | 0.0005118 | 0.0010345 | 8.30e-06 | 0.0007898 | 0.0002522 | 0.0010827 | 1.00e-05 | 0.0007490 | 0.0005110 | 1.41e-05 | 5.66e-05 | 0.0002538 | 1.08e-05 | 2.66e-05 | 1.33e-05 | 4.41e-05 | 8.0e-07 | 0.0005709 | 0.0002413 | 3.16e-05 | 6.7e-06 | 4.16e-05 | 4.49e-05 | 0.0001257 | 0.0001057 | 4.74e-05 | 5.0e-06 | 0.0002630 | 0.0001748 | 0.0000832 | 7.5e-06 | 0.0028529 | 8.0e-07 | 0.2312992 | 0.0002297 | 0.0004169 | 4.49e-05 | 0.0002430 | 0.0001323 | 0.0005426 | 0.0012242 | 0.0005243 | 1.58e-05 | 0.0002189 | 6.57e-05 | 0.0003961 | 0.0005368 | 1.08e-05 | 4.41e-05 | 0.0001806 | 0.0001997 | 5.8e-06 | 3.3e-06 | 9.40e-05 | 5.74e-05 | 0.0002355 | 0.0001573 | 0.0002480 | 3.74e-05 | 8.0e-07 | 0.0001656 | 0.0003645 | 0.0002372 | 0.0002056 | 9.40e-05 | 1.66e-05 | 0.0001456 | 0.0002646 | 0.0101514 | 3.30e-06 | 0.0004635 | 0.0001573 | 1.83e-05 | 4.49e-05 | 0.0001207 | 0.0002155 | 0.0001223 | 0.0483745 | 8.0e-07 | 1.25e-05 | 2.5e-06 | 6.32e-05 | 3.41e-05 | 0.0002255 | 0.0001165 | 4.2e-06 | 9.20e-06 | 0.0001390 | 0.0001307 | 0.0008505 | 0.0001648 | 0.0000674 | 1.66e-05 | 9.2e-06 | 3.91e-05 | 5.0e-06 | 0.0006849 | 1.50e-05 | 0.0092501 | 0.0002788 | 0.0015912 | 0.0012483 | 0.0009063 | 5.24e-05 | 4.49e-05 | 0.0001856 | 0.0028062 | 8.82e-05 | 0.0001015 | 0.0002239 | 0.0001748 | 5.16e-05 | 9.99e-05 | 6.32e-05 | 0.0009812 | 0.0006733 | 6.08e-05 | 0.0026731 | 1.83e-05 | 4.74e-05 | 0.0001257 | 3.83e-05 | 0.0002264 | 0.0075358 | 0.0006533 | 0.0001157 | 0.0210211 | 2.25e-05 | 6.99e-05 | 5.08e-05 | 0.0001581 | 0.0000999 | 9.32e-05 | 9.82e-05 | 0.0009737 | 0.0007082 | 0.0001365 | 0.0002788 | 0.0011318 | 0.0003412 | 0.0002929 | 1.50e-05 | 4.2e-06 | 7.50e-06 | 8.66e-05 | 6.66e-05 | 7.5e-06 | 1.25e-05 | 2.41e-05 | 5.0e-06 | 5.49e-05 | 9.2e-06 | 0.0002971 | 0.0002413 | 0.0008139 | 0.0019957 | 0.0008655 | 0.0001631 | 0.0001615 | 7.24e-05 | 0.0002014 | 3.50e-05 | 0.0001240 | 2.75e-05 | 9.24e-05 | 9.15e-05 | 8.41e-05 | 2.5e-06 | 0.0006816 | 0.0012350 | 0.0002971 | 4.2e-06 | 0.0001315 | 0.0006699 | 0.0001956 | 0.0001357 | 0.0000965 | 0.0001215 | 5.33e-05 | 0.0005576 | 6.70e-06 | 0.0017984 | 0.0001440 | 0.0002089 | 1.58e-05 | 2.16e-05 | 4.99e-05 | 1.58e-05 | 3.99e-05 | 5.0e-06 | 0.0003379 | 6.7e-06 | 6.66e-05 | 1.91e-05 | 9.20e-06 | 0.0000558 | 0.0039406 | 0.0000699 | 3.74e-05 | 5.8e-06 | 0.0014372 | 0.0003387 | 4.91e-05 | 7.5e-06 | 5.58e-05 | 6.08e-05 | 1.41e-05 | 6.16e-05 | 0.0002164 | 7.16e-05 | 0.0003021 | 0.0001049 | 0.0001723 | 0.0001007 | 2.50e-05 | 0.0006425 | 0.0004169 | 3.3e-06 | 5.24e-05 | 5.0e-06 | 5.33e-05 | 0.0003137 | 2.66e-05 | 6.41e-05 | 0.0007731 | 0.0003437 | 8.3e-06 | 0.0003146 | 1.58e-05 | 1.41e-05 | 4.91e-05 | 1.41e-05 | 0.0001731 | 0.0036817 | 7.74e-05 | 9.32e-05 | 0.0045048 | 0.0001615 | 0.0002364 | 0.0001689 | 0.0001939 | 0.0002971 | 0.0046013 | 1.08e-05 | 0.0000691 | 0.0000499 | 2.50e-06 | 3.41e-05 | 6.66e-05 | 4.2e-06 | 0.0092876 | 0.0003287 | 7.74e-05 | 0.0000458 | 3.91e-05 | 8.82e-05 | 2.58e-05 | 7.32e-05 | 9.57e-05 |
| ShotgunWGS-ControlPig5GutMicrobiome-Day7 | 5 | Control | Day 7 | Control Day 7 | 0.0012072 | 7.49e-05 | 0.0006482 | 2.08e-05 | 0.0002852 | 0.0002562 | 0.0000902 | 0.0096632 | 5.2e-06 | 2.93e-05 | 6.67e-05 | 7.91e-05 | 0.0001110 | 0.0000990 | 0.0003871 | 0.0001449 | 0.0002917 | 0.0005789 | 0.0002028 | 5.83e-05 | 0.0000729 | 1.53e-05 | 0.0005483 | 8.8e-06 | 1.01e-05 | 0.0001247 | 0.0001608 | 9.80e-06 | 6.2e-06 | 0.0003953 | 0.0001673 | 0.0001110 | 0.0000983 | 4.95e-05 | 0.0003809 | 0.0002106 | 0.0011405 | 0.0000879 | 0.0039948 | 6.67e-05 | 2.30e-06 | 0.0001087 | 0.0002058 | 3.42e-05 | 0.0002653 | 5.47e-05 | 0.0001459 | 4.53e-05 | 0.0010314 | 0.0004802 | 0.0003692 | 0.0012463 | 0.0028801 | 1.40e-05 | 0.0003718 | 0.0001426 | 0.0001703 | 0.0001426 | 0.0001498 | 0.0001354 | 0.0002393 | 1.6e-06 | 2.87e-05 | 2.83e-05 | 4.07e-05 | 0.0041645 | 3.22e-05 | 0.0001322 | 5.99e-05 | 7.16e-05 | 7.33e-05 | 2.0e-06 | 0.0072689 | 0.0733720 | 6.71e-05 | 0.0002191 | 0.0000957 | 2.18e-05 | 3.29e-05 | 2.08e-05 | 0.0000710 | 0.0215087 | 0.0000977 | 3.13e-05 | 0.0054932 | 0.0002702 | 0.0000853 | 7.80e-06 | 2.00e-06 | 0.0000661 | 0.0013678 | 0.0002178 | 0.0004516 | 0.0000697 | 3.42e-05 | 0.0000840 | 1.17e-05 | 6.58e-05 | 0.0003572 | 0.0008638 | 0.0094401 | 3.65e-05 | 0.0018766 | 0.0026456 | 0.0000794 | 9.80e-06 | 1.33e-05 | 0.0019229 | 1.17e-05 | 0.0001003 | 7.52e-05 | 0.0002631 | 1.86e-05 | 2.70e-05 | 0.0004040 | 1.86e-05 | 2.15e-05 | 0.0001853 | 8.5e-06 | 2.34e-05 | 5.70e-05 | 5.76e-05 | 1.73e-05 | 8.80e-06 | 1.6e-06 | 0.0003002 | 1.01e-05 | 0.0006124 | 0.0011304 | 2.51e-05 | 6.41e-05 | 0.0012369 | 6.28e-05 | 5.80e-05 | 0.0001778 | 0.0000622 | 0.0009132 | 0.0001084 | 7.80e-06 | 5.2e-06 | 0.0000794 | 0.0005675 | 2.80e-05 | 3.48e-05 | 3.68e-05 | 1.3e-06 | 0.0002136 | 0.0005825 | 0.0002299 | 0.0001426 | 2.9e-06 | 0.0001296 | 0.0001185 | 0.0000967 | 7.49e-05 | 1.01e-05 | 0.0001530 | 9.40e-06 | 0.0001009 | 2.60e-06 | 0.0886641 | 1.01e-05 | 0.0033316 | 0.0001162 | 0.0000632 | 9.67e-05 | 4.88e-05 | 6.20e-06 | 0.0003871 | 0.0061049 | 0.0000973 | 0.0000983 | 0.0005945 | 4.95e-05 | 0.0001410 | 4.88e-05 | 0.0001107 | 0.0008725 | 1.37e-05 | 0.0003116 | 2.0e-06 | 5.2e-06 | 0.0001953 | 1.3e-06 | 0.0004184 | 1.33e-05 | 0.0004356 | 4.6e-06 | 0.0002680 | 0.0001211 | 0.0008107 | 0.0000736 | 0.0002686 | 0.0001071 | 0.0001791 | 1.14e-05 | 0.0000908 | 0.0001980 | 0.0043077 | 0.0001374 | 0.0001680 | 0.0000850 | 0.0001449 | 1.82e-05 | 0.0002191 | 0.0027140 | 0.0024063 | 8.89e-05 | 6.77e-05 | 7.80e-06 | 0.0001556 | 0.0001696 | 0.0003060 | 0.0042192 | 7.94e-05 | 0.0001563 | 0.0002920 | 5.01e-05 | 3.97e-05 | 0.0001195 | 0.0107353 | 0.0004021 | 0.0001354 | 0.0025304 | 2.02e-05 | 1.56e-05 | 0.0001465 | 1.47e-05 | 1.0e-06 | 1.3e-06 | 1.20e-05 | 1.63e-05 | 0.0000716 | 0.0001644 | 0.0029729 | 2.47e-05 | 2.0e-06 | 0.0001755 | 4.23e-05 | 1.27e-05 | 0.0000967 | 0.0000726 | 0.0000964 | 0.0005408 | 0.0053714 | 0.0589401 | 0.0005489 | 0.0373568 | 0.0000879 | 2.54e-05 | 3.06e-05 | 0.0002038 | 0.0013153 | 0.0002188 | 1.60e-05 | 0.0007654 | 0.0010939 | 0.0001726 | 0.0002898 | 1.17e-05 | 0.0021713 | 5.93e-05 | 7.0e-07 | 0.0011454 | 5.63e-05 | 3.32e-05 | 0.0000687 | 0.0017737 | 0.0014238 | 5.34e-05 | 1.17e-05 | 2.47e-05 | 0.0001211 | 4.33e-05 | 4.98e-05 | 3.74e-05 | 0.0e+00 | 0.0004001 | 4.69e-05 | 0.0001400 | 4.9e-06 | 0.0004304 | 0.0001504 | 1.14e-05 | 0.0002432 | 0.0000902 | 2.80e-05 | 2.67e-05 | 1.37e-05 | 1.79e-05 | 1.27e-05 | 4.30e-05 | 1.66e-05 | 2.60e-05 | 6.87e-05 | 1.30e-05 | 2.25e-05 | 0.0005639 | 3.61e-05 | 0.0006163 | 0.0013590 | 7.81e-05 | 0.0000980 | 0.0001325 | 3.45e-05 | 0.0001957 | 1.14e-05 | 0.0035511 | 2.21e-05 | 3.26e-05 | 4.75e-05 | 1.33e-05 | 2.08e-05 | 6.12e-05 | 0.0001546 | 1.37e-05 | 7.8e-06 | 0.0006264 | 3.13e-05 | 4.40e-05 | 4.40e-05 | 0.0001130 | 0.0000733 | 0.0000791 | 6.22e-05 | 2.41e-05 | 0.0001022 | 1.99e-05 | 0.0002061 | 7.50e-06 | 0.0000423 | 5.01e-05 | 0.0001149 | 5.47e-05 | 4.62e-05 | 4.10e-05 | 7.0e-07 | 2.34e-05 | 6.2e-06 | 5.5e-06 | 0.1788730 | 0.0006108 | 0.0000264 | 6.12e-05 | 0.0001481 | 0.0003174 | 0.0003262 | 0.0001784 | 0.0000563 | 2.05e-05 | 5.67e-05 | 7e-07 | 0.0001579 | 0.0e+00 | 0.0000967 | 0.0006316 | 0.0003917 | 3.09e-05 | 0.0012903 | 4.20e-06 | 7.8e-06 | 1.66e-05 | 3.16e-05 | 2.18e-05 | 0.0003080 | 0.0001374 | 1.53e-05 | 0.0001374 | 0.0001944 | 5.90e-06 | 0.0000866 | 0.0002360 | 4.13e-05 | 0.0002015 | 0.0001843 | 2.44e-05 | 3.16e-05 | 0.0001589 | 0.0052210 | 0.0001566 | 0.0000560 | 0.0001074 | 1.63e-05 | 0.0006486 | 0.0001735 | 6.87e-05 | 0.0001107 | 0.0003865 | 0.0003588 | 0.0001175 | 3.09e-05 | 4.46e-05 | 0.0000970 | 3.84e-05 | 8.37e-05 | 0.0000957 | 0.0005125 | 0.0001104 | 4.66e-05 | 0.0001208 | 0.0001293 | 8.10e-06 | 2.64e-05 | 4.23e-05 | 0.0001205 | 9.83e-05 | 2.38e-05 | 0.0002002 | 4.59e-05 | 0.0001299 | 1.95e-05 | 3.42e-05 | 6.38e-05 | 4.79e-05 | 4.2e-06 | 3.91e-05 | 2.21e-05 | 8.33e-05 | 1.86e-05 | 7.03e-05 | 0.0001032 | 0.0165768 | 0.0003607 | 2.3e-06 | 1.82e-05 | 0.0011503 | 3.48e-05 | 0.0000544 | 0.0001628 | 0.0004845 | 0.0003305 | 0.0001605 | 2.0e-06 | 1.47e-05 | 5.18e-05 | 1.99e-05 | 1.3e-06 | 0.0004503 | 1.79e-05 | 2.44e-05 | 4.92e-05 | 5.9e-06 | 0.0002989 | 1.07e-05 | 3.09e-05 | 2.93e-05 | 1.60e-05 | 0.0000964 | 0.0001029 | 3.78e-05 | 0.0001638 | 0.0001514 | 1.01e-05 | 9.60e-05 | 6.28e-05 | 0.0000674 | 0.0001345 | 5.27e-05 | 2.02e-05 | 0.0001996 | 0.0000944 | 6.5e-06 | 2.47e-05 | 3.61e-05 | 4.66e-05 | 0.0004350 | 0.0000713 | 1.95e-05 | 1.3e-06 | 0.0002256 | 2.96e-05 | 0.0029943 | 0.0002793 | 0.0015901 | 1.11e-05 | 2.38e-05 | 2.87e-05 | 3.65e-05 | 0.0002859 | 0.0000007 | 1.0e-06 | 7.0e-07 | 0.0023002 | 0.0012714 | 0.0001608 | 0.0071679 | 9.80e-06 | 2.0e-06 | 7.36e-05 | 1.27e-05 | 0.0002260 | 7.42e-05 | 3.26e-05 | 0.0001774 | 9.1e-06 | 0.0002246 | 0.0003835 | 0.0008931 | 1.11e-05 | 0.0006974 | 0.0002618 | 0.0011070 | 9.10e-06 | 0.0004975 | 0.0004252 | 1.17e-05 | 5.60e-05 | 0.0001966 | 5.20e-06 | 1.53e-05 | 9.80e-06 | 3.35e-05 | 1.0e-06 | 0.0003484 | 0.0001599 | 2.60e-05 | 3.9e-06 | 3.32e-05 | 7.98e-05 | 0.0001416 | 0.0000283 | 3.09e-05 | 3.9e-06 | 0.0002618 | 0.0002728 | 0.0001182 | 6.2e-06 | 0.0025476 | 2.0e-06 | 0.1651427 | 0.0002930 | 0.0002146 | 4.82e-05 | 0.0001400 | 0.0000824 | 0.0003090 | 0.0009214 | 0.0005196 | 1.73e-05 | 0.0001494 | 4.92e-05 | 0.0002377 | 0.0005493 | 2.90e-06 | 3.22e-05 | 0.0001390 | 0.0002292 | 6.8e-06 | 7.0e-07 | 6.87e-05 | 3.71e-05 | 0.0002116 | 0.0002188 | 0.0002009 | 2.67e-05 | 1.0e-06 | 0.0001927 | 0.0003321 | 0.0001905 | 0.0001885 | 6.97e-05 | 9.40e-06 | 0.0001322 | 0.0002061 | 0.0153119 | 1.11e-05 | 0.0004421 | 0.0001289 | 2.83e-05 | 5.18e-05 | 0.0000697 | 0.0001914 | 0.0001286 | 0.0422350 | 1.3e-06 | 1.27e-05 | 7.0e-07 | 3.65e-05 | 2.31e-05 | 0.0001872 | 0.0001143 | 2.6e-06 | 1.11e-05 | 0.0001234 | 0.0001127 | 0.0002725 | 0.0001127 | 0.0001856 | 1.50e-05 | 8.5e-06 | 1.92e-05 | 4.9e-06 | 0.0006779 | 1.66e-05 | 0.0068267 | 0.0001872 | 0.0010484 | 0.0000680 | 0.0009028 | 5.80e-05 | 2.21e-05 | 0.0001973 | 0.0030057 | 5.67e-05 | 0.0001107 | 0.0001895 | 0.0001761 | 5.18e-05 | 9.90e-05 | 7.55e-05 | 0.0007739 | 0.0005623 | 5.31e-05 | 0.0013756 | 1.76e-05 | 3.58e-05 | 0.0000866 | 4.53e-05 | 0.0002100 | 0.0133708 | 0.0005942 | 0.0000876 | 0.0141685 | 1.86e-05 | 4.07e-05 | 3.81e-05 | 0.0001328 | 0.0000964 | 8.47e-05 | 9.31e-05 | 0.0009048 | 0.0013508 | 0.0001280 | 0.0002507 | 0.0009702 | 0.0003187 | 0.0002953 | 1.37e-05 | 2.3e-06 | 6.80e-06 | 5.57e-05 | 5.99e-05 | 2.0e-06 | 7.80e-06 | 1.92e-05 | 6.2e-06 | 8.30e-05 | 4.6e-06 | 0.0002735 | 0.0001993 | 0.0007003 | 0.0017239 | 0.0008599 | 0.0001302 | 0.0001061 | 6.80e-05 | 0.0001748 | 2.80e-05 | 0.0000944 | 2.67e-05 | 8.14e-05 | 6.02e-05 | 7.07e-05 | 3.6e-06 | 0.0005691 | 0.0008680 | 0.0002946 | 1.6e-06 | 0.0001087 | 0.0006219 | 0.0001566 | 0.0001133 | 0.0001055 | 0.0000912 | 3.61e-05 | 0.0003002 | 7.20e-06 | 0.0011174 | 0.0001091 | 0.0001520 | 8.80e-06 | 1.92e-05 | 5.37e-05 | 1.50e-05 | 3.39e-05 | 4.6e-06 | 0.0002653 | 4.2e-06 | 5.21e-05 | 1.20e-05 | 5.90e-06 | 0.0001048 | 0.0042954 | 0.0001094 | 5.93e-05 | 1.3e-06 | 0.0009344 | 0.0003793 | 3.22e-05 | 7.2e-06 | 3.22e-05 | 3.87e-05 | 7.50e-06 | 4.72e-05 | 0.0000964 | 8.24e-05 | 0.0002540 | 0.0000628 | 0.0001169 | 0.0000964 | 1.69e-05 | 0.0003506 | 0.0003139 | 5.5e-06 | 6.25e-05 | 3.9e-06 | 4.33e-05 | 0.0003220 | 4.04e-05 | 3.68e-05 | 0.0002735 | 0.0003340 | 9.8e-06 | 0.0001953 | 1.40e-05 | 7.20e-06 | 3.32e-05 | 8.50e-06 | 0.0001201 | 0.0034580 | 6.19e-05 | 7.00e-05 | 0.0043846 | 0.0001260 | 0.0001605 | 0.0001384 | 0.0001136 | 0.0001976 | 0.0048931 | 1.04e-05 | 0.0000563 | 0.0000619 | 2.00e-06 | 2.41e-05 | 3.09e-05 | 3.3e-06 | 0.0079575 | 0.0002338 | 4.20e-05 | 0.0000641 | 4.53e-05 | 3.97e-05 | 1.33e-05 | 6.51e-05 | 4.20e-06 |
| ShotgunWGS-TomatoPig18GutMicrobiome-Day7 | 18 | Tomato | Day 7 | Tomato Day 7 | 0.0006501 | 8.19e-05 | 0.0003281 | 1.85e-05 | 0.0001486 | 0.0001896 | 0.0001094 | 0.0059482 | 3.4e-06 | 4.77e-05 | 7.80e-05 | 7.57e-05 | 0.0000959 | 0.0001464 | 0.0006512 | 0.0000813 | 0.0002064 | 0.0003556 | 0.0002956 | 9.65e-05 | 0.0000561 | 2.75e-05 | 0.0001997 | 6.7e-06 | 7.30e-06 | 0.0000645 | 0.0001498 | 9.50e-06 | 5.0e-06 | 0.0004022 | 0.0002872 | 0.0000583 | 0.0001049 | 7.74e-05 | 0.0002737 | 0.0001032 | 0.0009177 | 0.0000780 | 0.0024922 | 5.05e-05 | 5.16e-05 | 0.0000415 | 0.0001099 | 1.91e-05 | 0.0001262 | 8.30e-05 | 0.0001285 | 2.97e-05 | 0.0007864 | 0.0002524 | 0.0003326 | 0.0008061 | 0.0016144 | 1.51e-05 | 0.0002620 | 0.0001027 | 0.0001369 | 0.0000830 | 0.0001088 | 0.0001027 | 0.0003999 | 1.1e-06 | 2.08e-05 | 4.43e-05 | 5.83e-05 | 0.0085061 | 2.64e-05 | 0.0001105 | 6.17e-05 | 5.89e-05 | 4.71e-05 | 2.8e-06 | 0.0049581 | 0.0944738 | 3.76e-05 | 0.0001200 | 0.0000819 | 1.46e-05 | 4.09e-05 | 1.12e-05 | 0.0001139 | 0.0585603 | 0.0000987 | 2.52e-05 | 0.0040758 | 0.0002507 | 0.0000752 | 9.50e-06 | 7.30e-06 | 0.0000931 | 0.0011628 | 0.0002592 | 0.0002816 | 0.0001212 | 5.72e-05 | 0.0000752 | 2.75e-05 | 1.80e-05 | 0.0002120 | 0.0007567 | 0.0061681 | 3.25e-05 | 0.0012464 | 0.0018191 | 0.0000611 | 7.90e-06 | 6.20e-06 | 0.0006967 | 5.60e-06 | 0.0000724 | 4.88e-05 | 0.0002345 | 1.07e-05 | 1.40e-05 | 0.0002406 | 4.50e-06 | 1.40e-05 | 0.0001975 | 3.9e-06 | 2.41e-05 | 2.69e-05 | 3.87e-05 | 1.51e-05 | 2.20e-06 | 0.0e+00 | 0.0004336 | 5.60e-06 | 0.0006372 | 0.0006221 | 1.12e-05 | 3.20e-05 | 0.0006283 | 9.98e-05 | 3.25e-05 | 0.0002098 | 0.0001127 | 0.0004213 | 0.0001251 | 6.20e-06 | 2.2e-06 | 0.0000954 | 0.0006995 | 2.19e-05 | 3.59e-05 | 2.86e-05 | 0.0e+00 | 0.0001868 | 0.0005901 | 0.0002087 | 0.0001402 | 9.0e-06 | 0.0000970 | 0.0000740 | 0.0001139 | 9.42e-05 | 7.30e-06 | 0.0002575 | 1.07e-05 | 0.0002227 | 8.40e-06 | 0.0567714 | 6.70e-06 | 0.0087798 | 0.0000836 | 0.0001027 | 8.41e-05 | 3.76e-05 | 6.70e-06 | 0.0002182 | 0.0031340 | 0.0000567 | 0.0000965 | 0.0010394 | 4.09e-05 | 0.0001144 | 3.93e-05 | 0.0001060 | 0.0009912 | 5.60e-06 | 0.0003332 | 6.0e-07 | 3.4e-06 | 0.0003949 | 2.8e-06 | 0.0003444 | 1.12e-05 | 0.0004426 | 6.2e-06 | 0.0001515 | 0.0000583 | 0.0005839 | 0.0001290 | 0.0002485 | 0.0001391 | 0.0001301 | 2.08e-05 | 0.0000595 | 0.0001172 | 0.0027587 | 0.0001094 | 0.0001335 | 0.0000611 | 0.0000976 | 1.85e-05 | 0.0001677 | 0.0016553 | 0.0010131 | 5.10e-05 | 6.45e-05 | 7.90e-06 | 0.0001217 | 0.0000578 | 0.0001761 | 0.0018118 | 5.67e-05 | 0.0001111 | 0.0002373 | 3.48e-05 | 4.49e-05 | 0.0001234 | 0.0048387 | 0.0004465 | 0.0000724 | 0.0024844 | 1.68e-05 | 1.12e-05 | 0.0000869 | 1.57e-05 | 0.0e+00 | 6.0e-07 | 3.40e-06 | 7.90e-06 | 0.0000174 | 0.0001975 | 0.0023369 | 1.68e-05 | 0.0e+00 | 0.0000954 | 2.30e-05 | 1.57e-05 | 0.0000898 | 0.0000359 | 0.0001200 | 0.0090541 | 0.0048308 | 0.0372300 | 0.0003798 | 0.0766393 | 0.0000337 | 1.96e-05 | 1.74e-05 | 0.0001374 | 0.0011617 | 0.0000948 | 2.24e-05 | 0.0004942 | 0.0016200 | 0.0001251 | 0.0004297 | 1.01e-05 | 0.0013552 | 3.70e-05 | 2.8e-06 | 0.0027166 | 2.47e-05 | 5.10e-05 | 0.0001167 | 0.0011976 | 0.0011370 | 8.98e-05 | 2.20e-06 | 3.93e-05 | 0.0001296 | 4.09e-05 | 5.95e-05 | 5.16e-05 | 6.0e-07 | 0.0003803 | 7.52e-05 | 0.0000836 | 3.4e-06 | 0.0002418 | 0.0000948 | 7.90e-06 | 0.0001257 | 0.0000808 | 3.03e-05 | 1.12e-05 | 7.90e-06 | 1.12e-05 | 1.18e-05 | 3.37e-05 | 1.46e-05 | 1.23e-05 | 8.02e-05 | 7.90e-06 | 2.19e-05 | 0.0003607 | 3.81e-05 | 0.0005200 | 0.0007629 | 9.70e-05 | 0.0000797 | 0.0001369 | 2.24e-05 | 0.0001027 | 8.40e-06 | 0.0027587 | 1.07e-05 | 1.46e-05 | 3.98e-05 | 7.90e-06 | 4.71e-05 | 5.55e-05 | 0.0000684 | 5.00e-06 | 3.4e-06 | 0.0003579 | 7.29e-05 | 9.37e-05 | 4.94e-05 | 0.0001105 | 0.0001335 | 0.0000578 | 3.03e-05 | 2.47e-05 | 0.0001829 | 5.00e-06 | 0.0003046 | 1.29e-05 | 0.0000639 | 4.82e-05 | 0.0000797 | 9.82e-05 | 3.48e-05 | 6.00e-05 | 1.1e-06 | 2.52e-05 | 2.8e-06 | 9.0e-06 | 0.1042975 | 0.0005228 | 0.0001430 | 4.15e-05 | 0.0001015 | 0.0003433 | 0.0004684 | 0.0001329 | 0.0001071 | 1.74e-05 | 2.86e-05 | 0e+00 | 0.0001318 | 1.1e-06 | 0.0001879 | 0.0004465 | 0.0003758 | 4.04e-05 | 0.0009199 | 1.51e-05 | 6.2e-06 | 2.36e-05 | 1.68e-05 | 1.63e-05 | 0.0002059 | 0.0000735 | 7.30e-06 | 0.0001127 | 0.0001509 | 5.60e-06 | 0.0000684 | 0.0002485 | 4.60e-05 | 0.0001257 | 0.0001167 | 1.29e-05 | 2.58e-05 | 0.0001357 | 0.0064643 | 0.0001402 | 0.0000370 | 0.0001481 | 1.12e-05 | 0.0003876 | 0.0000987 | 3.93e-05 | 0.0000892 | 0.0002395 | 0.0002721 | 0.0000959 | 2.19e-05 | 2.08e-05 | 0.0000645 | 3.14e-05 | 6.84e-05 | 0.0000533 | 0.0002850 | 0.0000959 | 3.87e-05 | 0.0000791 | 0.0000825 | 1.07e-05 | 1.01e-05 | 4.04e-05 | 0.0002311 | 7.24e-05 | 2.80e-05 | 0.0002474 | 7.24e-05 | 0.0001245 | 8.40e-06 | 7.01e-05 | 4.04e-05 | 3.98e-05 | 5.6e-06 | 4.99e-05 | 2.30e-05 | 8.13e-05 | 1.46e-05 | 8.47e-05 | 0.0000864 | 0.0049536 | 0.0005694 | 3.4e-06 | 1.35e-05 | 0.0006754 | 1.63e-05 | 0.0000191 | 0.0001784 | 0.0007500 | 0.0002221 | 0.0001834 | 0.0e+00 | 1.57e-05 | 8.86e-05 | 2.13e-05 | 6.0e-07 | 0.0002850 | 1.07e-05 | 7.90e-06 | 2.36e-05 | 5.0e-06 | 0.0001778 | 7.90e-06 | 3.20e-05 | 1.07e-05 | 9.50e-06 | 0.0000628 | 0.0001172 | 3.31e-05 | 0.0001285 | 0.0001464 | 8.40e-06 | 6.28e-05 | 4.54e-05 | 0.0000993 | 0.0002754 | 6.84e-05 | 1.29e-05 | 0.0001946 | 0.0001335 | 7.3e-06 | 3.03e-05 | 4.66e-05 | 4.43e-05 | 0.0002765 | 0.0000774 | 1.23e-05 | 0.0e+00 | 0.0001930 | 3.48e-05 | 0.0061911 | 0.0004028 | 0.0009514 | 7.30e-06 | 2.19e-05 | 2.02e-05 | 2.92e-05 | 0.0000864 | 0.0000673 | 1.1e-06 | 8.4e-06 | 0.0015790 | 0.0017703 | 0.0000987 | 0.0087316 | 8.40e-06 | 6.0e-07 | 6.23e-05 | 1.29e-05 | 0.0004970 | 8.02e-05 | 3.03e-05 | 0.0000639 | 9.5e-06 | 0.0001380 | 0.0004347 | 0.0009979 | 1.68e-05 | 0.0005082 | 0.0002822 | 0.0006815 | 7.30e-06 | 0.0002535 | 0.0002137 | 1.40e-05 | 4.21e-05 | 0.0001397 | 6.70e-06 | 2.02e-05 | 9.50e-06 | 3.98e-05 | 1.1e-06 | 0.0001616 | 0.0000869 | 1.40e-05 | 3.9e-06 | 2.19e-05 | 9.42e-05 | 0.0001700 | 0.0000409 | 3.93e-05 | 6.2e-06 | 0.0002889 | 0.0005312 | 0.0001862 | 3.4e-06 | 0.0026594 | 2.2e-06 | 0.2682445 | 0.0004056 | 0.0003629 | 3.37e-05 | 0.0000746 | 0.0000337 | 0.0001874 | 0.0008173 | 0.0003281 | 8.40e-06 | 0.0000993 | 5.22e-05 | 0.0000993 | 0.0003136 | 3.90e-06 | 2.36e-05 | 0.0001004 | 0.0002132 | 6.2e-06 | 1.1e-06 | 4.09e-05 | 6.23e-05 | 0.0002042 | 0.0002563 | 0.0002760 | 3.20e-05 | 0.0e+00 | 0.0002008 | 0.0003012 | 0.0001526 | 0.0001767 | 7.18e-05 | 2.20e-06 | 0.0001027 | 0.0002047 | 0.0088533 | 9.50e-06 | 0.0004117 | 0.0001498 | 3.76e-05 | 5.16e-05 | 0.0000864 | 0.0002025 | 0.0001212 | 0.0264808 | 6.0e-07 | 5.00e-06 | 3.9e-06 | 4.60e-05 | 6.51e-05 | 0.0002350 | 0.0002031 | 1.1e-06 | 1.07e-05 | 0.0001105 | 0.0001851 | 0.0006002 | 0.0001700 | 0.0003214 | 1.35e-05 | 2.2e-06 | 2.24e-05 | 4.5e-06 | 0.0004824 | 1.29e-05 | 0.0013463 | 0.0001386 | 0.0006204 | 0.0011297 | 0.0005278 | 4.71e-05 | 3.59e-05 | 0.0001986 | 0.0028008 | 2.58e-05 | 0.0001077 | 0.0002216 | 0.0001481 | 7.12e-05 | 1.29e-04 | 9.48e-05 | 0.0004717 | 0.0008341 | 5.67e-05 | 0.0016879 | 1.29e-05 | 4.43e-05 | 0.0000813 | 6.34e-05 | 0.0002008 | 0.0057126 | 0.0008779 | 0.0001212 | 0.0215249 | 2.52e-05 | 2.97e-05 | 2.41e-05 | 0.0001419 | 0.0000561 | 3.93e-05 | 4.21e-05 | 0.0006226 | 0.0019566 | 0.0001156 | 0.0001705 | 0.0007130 | 0.0001666 | 0.0001784 | 2.08e-05 | 2.8e-06 | 2.20e-06 | 4.94e-05 | 4.54e-05 | 2.2e-06 | 5.60e-06 | 1.57e-05 | 3.4e-06 | 6.45e-05 | 1.7e-06 | 0.0001924 | 0.0001251 | 0.0003489 | 0.0011656 | 0.0005121 | 0.0001111 | 0.0001694 | 8.98e-05 | 0.0000959 | 1.74e-05 | 0.0000909 | 2.30e-05 | 7.91e-05 | 8.47e-05 | 4.94e-05 | 2.2e-06 | 0.0003237 | 0.0003781 | 0.0001750 | 5.6e-06 | 0.0001060 | 0.0004005 | 0.0001228 | 0.0000909 | 0.0000847 | 0.0000471 | 4.99e-05 | 0.0000729 | 5.60e-06 | 0.0008106 | 0.0000875 | 0.0001043 | 1.63e-05 | 2.13e-05 | 6.23e-05 | 1.35e-05 | 4.66e-05 | 5.6e-06 | 0.0001178 | 1.7e-06 | 3.76e-05 | 2.13e-05 | 1.46e-05 | 0.0001733 | 0.0028142 | 0.0001761 | 8.47e-05 | 1.7e-06 | 0.0005396 | 0.0001329 | 4.04e-05 | 3.4e-06 | 2.69e-05 | 3.81e-05 | 6.20e-06 | 3.59e-05 | 0.0000701 | 8.64e-05 | 0.0002721 | 0.0000376 | 0.0001840 | 0.0000595 | 1.01e-05 | 0.0003068 | 0.0004129 | 6.7e-06 | 6.06e-05 | 2.8e-06 | 7.80e-05 | 0.0001761 | 2.97e-05 | 5.60e-06 | 0.0006776 | 0.0002193 | 2.8e-06 | 0.0000668 | 8.40e-06 | 9.50e-06 | 2.30e-05 | 6.70e-06 | 0.0000707 | 0.0024249 | 4.71e-05 | 4.21e-05 | 0.0022275 | 0.0000791 | 0.0001526 | 0.0001638 | 0.0000920 | 0.0001531 | 0.0029915 | 3.40e-06 | 0.0000589 | 0.0000561 | 1.07e-05 | 1.80e-05 | 1.40e-05 | 4.5e-06 | 0.0069882 | 0.0002317 | 2.69e-05 | 0.0001021 | 4.54e-05 | 6.70e-06 | 5.00e-06 | 4.88e-05 | 3.31e-05 |
# move Sample_Name to rownames, remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt
rownames(RelAbund.Genus.Filt.zerofilt.alphadiv) <- RelAbund.Genus.Filt.zerofilt.alphadiv$Sample_Name
RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:8]
## Sample_Name
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 ShotgunWGS-ControlPig6GutMicrobiome-Day14
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 ShotgunWGS-ControlPig8GutMicrobiome-Day0
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 ShotgunWGS-ControlPig3GutMicrobiome-Day14
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 ShotgunWGS-TomatoPig14GutMicrobiome-Day7
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 ShotgunWGS-ControlPig5GutMicrobiome-Day7
## Pig Diet Time_Point
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## Diet_By_Time_Point Abiotrophia
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 Control Day 14 0.001305713
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 Control Day 0 0.001347804
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 Control Day 14 0.001066255
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 Tomato Day 7 0.001311580
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 Control Day 7 0.001207244
## Acaryochloris Acetivibrio
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 6.983388e-05 0.0005122869
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 9.904370e-05 0.0007097339
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 6.914992e-05 0.0005363651
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 1.090209e-04 0.0008422076
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 7.488298e-05 0.0006482261
# remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt.alphadiv %>%
select(Abiotrophia:ncol(.))
RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:5]
## Abiotrophia Acaryochloris
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 0.001305713 6.983388e-05
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 0.001347804 9.904370e-05
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 0.001066255 6.914992e-05
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 0.001311580 1.090209e-04
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 0.001207244 7.488298e-05
## Acetivibrio Acetobacter
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 0.0005122869 1.700751e-05
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 0.0007097339 2.047538e-05
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 0.0005363651 1.553931e-05
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 0.0008422076 3.412106e-05
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 0.0006482261 2.083700e-05
## Acetohalobium
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 0.0002669664
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 0.0003268918
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 0.0002628733
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 0.0003320562
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 0.0002852065
rownames(RelAbund.Genus.Filt.zerofilt.alphadiv)
## [1] "ShotgunWGS-ControlPig6GutMicrobiome-Day14"
## [2] "ShotgunWGS-ControlPig8GutMicrobiome-Day0"
## [3] "ShotgunWGS-ControlPig3GutMicrobiome-Day14"
## [4] "ShotgunWGS-TomatoPig14GutMicrobiome-Day7"
## [5] "ShotgunWGS-ControlPig5GutMicrobiome-Day7"
## [6] "ShotgunWGS-TomatoPig18GutMicrobiome-Day7"
## [7] "ShotgunWGS-TomatoPig16GutMicrobiome-Day7"
## [8] "ShotgunWGS-ControlPig10GutMicrobiome-Day7"
## [9] "ShotgunWGS-ControlPig2GutMicrobiome-Day0"
## [10] "ShotgunWGS-TomatoPig18GutMicrobiome-Day0"
## [11] "ShotgunWGS-ControlPig10GutMicrobiome-Day0"
## [12] "ShotgunWGS-ControlPig7GutMicrobiome-Day0"
## [13] "ShotgunWGS-ControlPig8GutMicrobiome-Day14"
## [14] "ShotgunWGS-TomatoPig11GutMicrobiome-Day0"
## [15] "ShotgunWGS-TomatoPig19GutMicrobiome-Day0"
## [16] "ShotgunWGS-TomatoPig17GutMicrobiome-Day14"
## [17] "ShotgunWGS-ControlPig9GutMicrobiome-Day14"
## [18] "ShotgunWGS-ControlPig10GutMicrobiome-Day14"
## [19] "ShotgunWGS-TomatoPig19GutMicrobiome-Day7"
## [20] "ShotgunWGS-ControlPig5GutMicrobiome-Day14"
## [21] "ShotgunWGS-ControlPig2GutMicrobiome-Day7"
## [22] "ShotgunWGS-ControlPig6GutMicrobiome-Day7"
## [23] "ShotgunWGS-TomatoPig12GutMicrobiome-Day0"
## [24] "ShotgunWGS-TomatoPig14GutMicrobiome-Day0"
## [25] "ShotgunWGS-ControlPig7GutMicrobiome-Day14"
## [26] "ShotgunWGS-TomatoPig11GutMicrobiome-Day14"
## [27] "ShotgunWGS-TomatoPig20GutMicrobiome-Day0"
## [28] "ShotgunWGS-ControlPig9GutMicrobiome-Day0"
## [29] "ShotgunWGS-TomatoPig11GutMicrobiome-Day7"
## [30] "ShotgunWGS-TomatoPig13GutMicrobiome-Day7"
## [31] "ShotgunWGS-TomatoPig17GutMicrobiome-Day0"
## [32] "ShotgunWGS-TomatoPig19GutMicrobiome-Day14"
## [33] "ShotgunWGS-TomatoPig13GutMicrobiome-Day0"
## [34] "ShotgunWGS-ControlPig2GutMicrobiome-Day14"
## [35] "ShotgunWGS-ControlPig1GutMicrobiome-Day7"
## [36] "ShotgunWGS-TomatoPig15GutMicrobiome-Day7"
## [37] "ShotgunWGS-TomatoPig15GutMicrobiome-Day0"
## [38] "ShotgunWGS-TomatoPig12GutMicrobiome-Day7"
## [39] "ShotgunWGS-TomatoPig14GutMicrobiome-Day14"
## [40] "ShotgunWGS-TomatoPig20GutMicrobiome-Day14"
## [41] "ShotgunWGS-ControlPig1GutMicrobiome-Day0"
## [42] "ShotgunWGS-ControlPig4GutMicrobiome-Day14"
## [43] "ShotgunWGS-ControlPig6GutMicrobiome-Day0"
## [44] "ShotgunWGS-TomatoPig16GutMicrobiome-Day0"
## [45] "ShotgunWGS-TomatoPig16GutMicrobiome-Day14"
## [46] "ShotgunWGS-TomatoPig18GutMicrobiome-Day14"
## [47] "ShotgunWGS-ControlPig7GutMicrobiome-Day7"
## [48] "ShotgunWGS-ControlPig4GutMicrobiome-Day7"
## [49] "ShotgunWGS-TomatoPig13GutMicrobiome-Day14"
## [50] "ShotgunWGS-ControlPig8GutMicrobiome-Day7"
## [51] "ShotgunWGS-TomatoPig15GutMicrobiome-Day14"
## [52] "ShotgunWGS-TomatoPig12GutMicrobiome-Day14"
## [53] "ShotgunWGS-TomatoPig20GutMicrobiome-Day7"
## [54] "ShotgunWGS-ControlPig1GutMicrobiome-Day14"
## [55] "ShotgunWGS-ControlPig3GutMicrobiome-Day0"
## [56] "ShotgunWGS-ControlPig5GutMicrobiome-Day0"
## [57] "ShotgunWGS-ControlPig4GutMicrobiome-Day0"
## [58] "ShotgunWGS-ControlPig9GutMicrobiome-Day7"
## [59] "ShotgunWGS-ControlPig3GutMicrobiome-Day7"
## [60] "ShotgunWGS-TomatoPig17GutMicrobime-Day7"
# run alpha diversity on phyla
genera.filt.div <- diversity(RelAbund.Genus.Filt.zerofilt.alphadiv, index = "shannon")
# convert to df
genera.filt.div.df <- as.data.frame(genera.filt.div)
# make column name 'shannon.phyla.filt'
colnames(genera.filt.div.df) <- "shannon.genera.filt"
head(genera.filt.div.df)
## shannon.genera.filt
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 3.454094
## ShotgunWGS-ControlPig8GutMicrobiome-Day0 3.724200
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 3.405044
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7 3.639925
## ShotgunWGS-ControlPig5GutMicrobiome-Day7 3.526991
## ShotgunWGS-TomatoPig18GutMicrobiome-Day7 3.281356
Combine shannon alpha diversity results with metadata
# compile genera metadata
genera.metadata <- RelAbund.Genus.Filt.zerofilt[,1:5]
# combine with metadata
genera.filt.div.df.meta <- cbind(genera.metadata, genera.filt.div.df)
head(genera.filt.div.df.meta)
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## 6 ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7
## Diet_By_Time_Point shannon.genera.filt
## 1 Control Day 14 3.454094
## 2 Control Day 0 3.724200
## 3 Control Day 14 3.405044
## 4 Tomato Day 7 3.639925
## 5 Control Day 7 3.526991
## 6 Tomato Day 7 3.281356
X axis by diet
alpha.diversity.genera.bydiet <- genera.filt.div.df.meta %>%
ggplot(aes(x = Diet, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
geom_boxplot(outlier.shape = NA) +
geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black")) +
labs(x=NULL,
y="Shannon diversity index",
title = "Alpha Diversity",
subtitle = "Shannon Index, Genera Level",
fill="Diet & Time Point")
alpha.diversity.genera.bydiet
ggsave("Figures/AlphaDiversityGenera_ByDiet_Boxplot.png",
plot = alpha.diversity.genera.bydiet,
dpi = 800,
width = 10,
height = 6)
X-axis by Day
genera.filt.div.df.meta <- genera.filt.div.df.meta %>%
mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
alpha.diversity.genera.bytime <- genera.filt.div.df.meta %>%
ggplot(aes(x = Time_Point, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
geom_boxplot(outlier.shape = NA) +
geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black")) +
labs(x=NULL,
y="Shannon diversity index",
title = "Alpha Diversity",
subtitle = "Shannon Index, Genera Level",
fill="Diet & Time Point")
alpha.diversity.genera.bytime
ggsave("Figures/AlphaDiversityGenera_ByTime_Boxplot.png",
plot = alpha.diversity.genera.bytime,
dpi = 800,
width = 7,
height = 5)
Repeated measures ANOVA on Shannon alpha diversity
# must remove columns that aren't used in anova
head(genera.filt.div.df.meta)
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## 6 ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7
## Diet_By_Time_Point shannon.genera.filt
## 1 Control Day 14 3.454094
## 2 Control Day 0 3.724200
## 3 Control Day 14 3.405044
## 4 Tomato Day 7 3.639925
## 5 Control Day 7 3.526991
## 6 Tomato Day 7 3.281356
genera.filt.div.df.meta.foranova <- genera.filt.div.df.meta[,-c(1,5)]
head(genera.filt.div.df.meta.foranova)
## Pig Diet Time_Point shannon.genera.filt
## 1 6 Control Day 14 3.454094
## 2 8 Control Day 0 3.724200
## 3 3 Control Day 14 3.405044
## 4 14 Tomato Day 7 3.639925
## 5 5 Control Day 7 3.526991
## 6 18 Tomato Day 7 3.281356
genera.filt.alphadiv.anova <-
anova_test(data = genera.filt.div.df.meta.foranova,
formula = shannon.genera.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
dv = shannon.genera.filt,
wid = Pig,
within = Time_Point,
between = Diet)
get_anova_table(genera.filt.alphadiv.anova)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 18 2.888 0.106 0.066
## 2 Time_Point 2 36 0.254 0.777 0.008
## 3 Diet:Time_Point 2 36 0.037 0.964 0.001
Check for normality
shapiro.test(genera.filt.div.df.meta.foranova$shannon.genera.filt)
##
## Shapiro-Wilk normality test
##
## data: genera.filt.div.df.meta.foranova$shannon.genera.filt
## W = 0.97004, p-value = 0.1466
Normal.
No need for posthoc test since no model parameters are significant.
Quick introduction to anatomy of the aldex function
The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, unchanged
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want to incude effect results in output?
Key to aldex outputs - taken directly from vignette
ALDEx2 takes counts, not relative abundance.
We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)
Downloading ALDEx2
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ALDEx2")
Since we use counts for ALDEx2, we need to filter our counts data to include only the genera we ended up using in our final analysis
# this is the data set filtered to remove inplausible phyla, but still includes genera with a lot of missing values
Genus.Counts.Filt[1:10,1:10]
## # A tibble: 10 × 10
## domain phylum class order family genus `ShotgunWGS-Co…` `ShotgunWGS-Co…`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Viruses unclass… uncl… Caud… Podov… AHJD… 29 0
## 2 Bacteria Firmicu… Baci… Lact… Aeroc… Abio… 5067 5661
## 3 Eukaryota unclass… uncl… uncl… uncla… Acan… 0 0
## 4 Bacteria Cyanoba… uncl… uncl… uncla… Acar… 271 416
## 5 Bacteria Firmicu… Clos… Clos… Rumin… Acet… 1988 2981
## 6 Bacteria Proteob… Alph… Rhod… Aceto… Acet… 66 86
## 7 Bacteria Firmicu… Clos… Hala… Halob… Acet… 1036 1373
## 8 Bacteria Teneric… Moll… Acho… Achol… Acho… 779 1269
## 9 Bacteria Proteob… Beta… Burk… Alcal… Achr… 192 298
## 10 Bacteria Firmicu… Nega… Sele… Acida… Acid… 50181 39909
## # … with 2 more variables: `ShotgunWGS-ControlPig3GutMicrobiome-Day14` <dbl>,
## # `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>
dim(Genus.Counts.Filt)
## [1] 895 66
# final genera list (after filtering for zeros)
final_genera[1:10,]
## [1] "Abiotrophia" "Acaryochloris" "Acetivibrio" "Acetobacter"
## [5] "Acetohalobium" "Acholeplasma" "Achromobacter" "Acidaminococcus"
## [9] "Acidilobus" "Acidimicrobium"
# how many final genera do we have?
dim(final_genera)
## [1] 755 1
# join to create a df with genera in rows, samples in columns
# filtered for genera used in this analysis
genera_counts_foraldex <- inner_join(final_genera, Genus.Counts.Filt,
by = "genus")
dim(genera_counts_foraldex)
## [1] 755 66
# remove non-necessary metadata
genera_counts_foraldex <- genera_counts_foraldex[,-c(2:6)]
genera_counts_foraldex[1:10, 1:4]
## genus ShotgunWGS-ControlPig6GutMicrobiome-Day14
## 1 Abiotrophia 5067
## 2 Acaryochloris 271
## 3 Acetivibrio 1988
## 4 Acetobacter 66
## 5 Acetohalobium 1036
## 6 Acholeplasma 779
## 7 Achromobacter 192
## 8 Acidaminococcus 50181
## 9 Acidilobus 10
## 10 Acidimicrobium 59
## ShotgunWGS-ControlPig8GutMicrobiome-Day0
## 1 5661
## 2 416
## 3 2981
## 4 86
## 5 1373
## 6 1269
## 7 298
## 8 39909
## 9 20
## 10 126
## ShotgunWGS-ControlPig3GutMicrobiome-Day14
## 1 4117
## 2 267
## 3 2071
## 4 60
## 5 1015
## 6 817
## 7 197
## 8 31994
## 9 25
## 10 81
# add genera as rownames
rownames(genera_counts_foraldex) <- genera_counts_foraldex$genus
# remove genera as column for cleaner data
genera_counts_foraldex <- genera_counts_foraldex %>%
select(-genus)
Look at the effect of diet at day 0.
# subset day 0 only
Day0.Counts.Genera.filt <- genera_counts_foraldex %>%
select(ends_with("Day0"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day0.Counts.Genera.filt <- Day0.Counts.Genera.filt[order(colnames(Day0.Counts.Genera.filt))]
Diets.Day0.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day0.Genera
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-test
filt.Genera.Day0.ByDiet.aldex <- aldex(Day0.Counts.Genera.filt,
Diets.Day0.Genera,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day0.ByDiet.aldex <-
filt.Genera.Day0.ByDiet.aldex[order(filt.Genera.Day0.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Genera.Day0.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Methanoplanus | 0.3201847 | 0.4587166 | 0.1585091 | -0.2744457 | 0.2929463 | -0.8900506 | 0.1761648 | 0.0161507 | 0.6571223 | 0.0206049 | 0.6391049 |
| Herbaspirillum | -0.5564932 | -0.7855297 | -0.3447862 | 0.4894062 | 0.5715672 | 0.8331084 | 0.1700000 | 0.0108595 | 0.6613759 | 0.0156222 | 0.6367983 |
| Caenorhabditis | -0.8681507 | -0.7231369 | -1.0313465 | -0.3344422 | 0.4358249 | -0.7236616 | 0.1917616 | 0.0233795 | 0.6654581 | 0.0326584 | 0.6445105 |
| Gallionella | -0.8191944 | -0.9806255 | -0.6565452 | 0.3378006 | 0.4275946 | 0.7312681 | 0.1807638 | 0.0233197 | 0.6656534 | 0.0259145 | 0.6331800 |
| Epsilon15-like viruses | -5.4155796 | -6.3291178 | -4.7770370 | 1.5282103 | 1.9392933 | 0.7162934 | 0.1777644 | 0.0342860 | 0.6681890 | 0.0276429 | 0.6289355 |
| Collinsella | 6.4097209 | 5.9122154 | 6.8773284 | 0.9055678 | 0.8808264 | 0.9277446 | 0.1979604 | 0.0098821 | 0.6683595 | 0.0208505 | 0.6554576 |
Create a histogram of pvalues of we.eBH
hist(filt.Genera.Day0.ByDiet.aldex$we.eBH,
breaks = 20,
main = "Histogram of p-values on the effect of diet at day 0 on genera",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
we.eBH is the Benjamini-Hochberg corrected p-value, no significantly different genera at day 0.
Look at the effect of diet at day 7.
# subset day 7 only
Day7.Counts.Genera.filt <- genera_counts_foraldex %>%
select(ends_with("Day7"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day7.Counts.Genera.filt <- Day7.Counts.Genera.filt[order(colnames(Day7.Counts.Genera.filt))]
Diets.Day7.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day7.Genera
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-test
filt.Genera.Day7.ByDiet.aldex <- aldex(Day7.Counts.Genera.filt,
Diets.Day7.Genera,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day7.ByDiet.aldex <-
filt.Genera.Day7.ByDiet.aldex[order(filt.Genera.Day7.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Genera.Day7.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| unclassified (derived from Bacteria) | 2.5026985 | 1.9874193 | 3.0144669 | 0.8894342 | 0.5458126 | 1.6215552 | 0.0466000 | 0.0000347 | 0.0256786 | 0.0001885 | 0.0816149 |
| Staphylococcus | 4.3981480 | 4.2392110 | 4.6647900 | 0.3930480 | 0.2840870 | 1.3716187 | 0.0507898 | 0.0003334 | 0.1056045 | 0.0002354 | 0.0877397 |
| Alphatorquevirus | -2.7795012 | -3.9782818 | -2.1901574 | 1.8729947 | 1.5103759 | 1.2115892 | 0.0950000 | 0.0013080 | 0.2092870 | 0.0017119 | 0.2325936 |
| Lambda-like viruses | -0.5334489 | -1.3489239 | 0.2540438 | 1.5967109 | 1.1783139 | 1.1878787 | 0.1027794 | 0.0013951 | 0.2330323 | 0.0017119 | 0.2490163 |
| Clavibacter | 0.7748387 | 0.5505235 | 1.0398215 | 0.6786631 | 0.6909331 | 0.9901394 | 0.1345731 | 0.0034204 | 0.3646270 | 0.0059299 | 0.4272165 |
| Kluyveromyces | -2.7486549 | -3.1193205 | -2.3047942 | 0.8797267 | 0.9542796 | 0.8722283 | 0.1534000 | 0.0137741 | 0.5092141 | 0.0189920 | 0.5001200 |
One genera was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.025
hist(filt.Genera.Day7.ByDiet.aldex$we.eBH,
breaks = 20,
main = "Histogram of p-values on the effect of diet at day 7 on genera",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
What is the directionality of the change?
filt.Genera.Day7.ByDiet.aldex %>%
select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
filter(we.eBH <= 0.05)
## rab.win.Control rab.win.Tomato we.eBH
## unclassified (derived from Bacteria) 1.987419 3.014467 0.02567862
Unclassified (derived from Bacteria) is higher in Tomato than Control.
Look at the effect of diet on day 14.
# subset day 14 only
Day14.Counts.Genera.filt <- genera_counts_foraldex %>%
select(ends_with("Day14"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day14.Counts.Genera.filt <- Day14.Counts.Genera.filt[order(colnames(Day14.Counts.Genera.filt))]
Diets.Day14.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day14.Genera
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-test
filt.Genera.Day14.ByDiet.aldex <- aldex(Day14.Counts.Genera.filt,
Diets.Day14.Genera,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day14.ByDiet.aldex <-
filt.Genera.Day14.ByDiet.aldex[order(filt.Genera.Day14.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Genera.Day14.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Lambda-like viruses | -0.6765788 | -2.014760 | 1.1151072 | 3.1367093 | 0.5031233 | 6.152552 | 0.000014 | 0.00e+00 | 0.0000000 | 0.0000108 | 0.0017403 |
| Staphylococcus | 4.4868458 | 4.203031 | 4.8287360 | 0.6626948 | 0.2017804 | 3.372094 | 0.000014 | 0.00e+00 | 0.0000020 | 0.0000108 | 0.0017403 |
| Alphatorquevirus | -2.7478328 | -4.775638 | -0.9952775 | 3.7616825 | 1.1479595 | 3.213701 | 0.000014 | 1.00e-07 | 0.0000166 | 0.0000108 | 0.0017403 |
| unclassified (derived from Bacteria) | 2.1148119 | 1.507804 | 2.9099037 | 1.3704930 | 0.5614568 | 2.482695 | 0.000014 | 3.00e-07 | 0.0000489 | 0.0000108 | 0.0017403 |
| Loa | -3.2560289 | -4.570576 | -2.2214430 | 2.3520024 | 1.2624078 | 1.786604 | 0.030000 | 6.60e-05 | 0.0053620 | 0.0001009 | 0.0075989 |
| Plasmodium | -0.4456836 | -1.018278 | -0.0948831 | 0.9253498 | 0.4646515 | 1.835934 | 0.060188 | 8.46e-05 | 0.0068115 | 0.0004468 | 0.0195314 |
hist(filt.Genera.Day14.ByDiet.aldex$we.eBH,
breaks = 20,
main = "Histogram of p-values on the effect of diet at day 14 on genera",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
How many significant genera are there?
filt.Day14.Genera.aldex.sig <- filt.Genera.Day14.ByDiet.aldex[which(filt.Genera.Day14.ByDiet.aldex$we.eBH<0.05),]
length(rownames(filt.Day14.Genera.aldex.sig))
## [1] 14
Which genera are they?
sig_day14_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Day14.Genera.aldex.sig),
filt.Day14.Genera.aldex.sig$we.eBH))
sig_day14_genera_aldex2 <- sig_day14_genera_aldex2 %>%
rename(Genera = V1,
we.eBH_pvalue = V2)
sig_day14_genera_aldex2
## Genera we.eBH_pvalue
## 1 Lambda-like viruses 3.90702968822257e-08
## 2 Staphylococcus 1.98833990718406e-06
## 3 Alphatorquevirus 1.66493484376377e-05
## 4 unclassified (derived from Bacteria) 4.88751914488331e-05
## 5 Loa 0.00536199352280373
## 6 Plasmodium 0.00681151568341803
## 7 Propionibacterium 0.00938576734150738
## 8 Saccharomyces 0.0162702517857512
## 9 Stenotrophomonas 0.0215525889312409
## 10 Malassezia 0.0222884879030356
## 11 Roseiflexus 0.0224147224012945
## 12 Brugia 0.0315547210562422
## 13 Streptococcus 0.0316064329890332
## 14 Vanderwaltozyma 0.0355364448144129
What is the directionality of the change?
filt.Genera.Day14.ByDiet.aldex %>%
select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
filter(we.eBH <= 0.05)
## rab.win.Control rab.win.Tomato
## Lambda-like viruses -2.01475973 1.1151072
## Staphylococcus 4.20303084 4.8287360
## Alphatorquevirus -4.77563841 -0.9952775
## unclassified (derived from Bacteria) 1.50780388 2.9099037
## Loa -4.57057605 -2.2214430
## Plasmodium -1.01827823 -0.0948831
## Propionibacterium 1.40929760 2.0214784
## Saccharomyces -1.92007049 0.3880179
## Stenotrophomonas 0.01556489 0.4808232
## Malassezia -3.61348687 -1.7990132
## Roseiflexus 2.61037473 2.3131767
## Brugia -3.16504650 -1.6096471
## Streptococcus 10.54449623 8.0218160
## Vanderwaltozyma -4.08112899 -1.1653644
## we.eBH
## Lambda-like viruses 3.907030e-08
## Staphylococcus 1.988340e-06
## Alphatorquevirus 1.664935e-05
## unclassified (derived from Bacteria) 4.887519e-05
## Loa 5.361994e-03
## Plasmodium 6.811516e-03
## Propionibacterium 9.385767e-03
## Saccharomyces 1.627025e-02
## Stenotrophomonas 2.155259e-02
## Malassezia 2.228849e-02
## Roseiflexus 2.241472e-02
## Brugia 3.155472e-02
## Streptococcus 3.160643e-02
## Vanderwaltozyma 3.553644e-02
All significantly different genera are higher in tomato as compared to control.
# subset control only samples across all time points, should be n=30
Control.Counts.Genera.filt <- genera_counts_foraldex %>%
select(contains("Control"))
dim(Control.Counts.Genera.filt)
## [1] 755 30
ALDEx2 function needs a factor of variables
# results in pigs at different time points being grouped together
Control.Counts.Genera.filt <- Control.Counts.Genera.filt[order(colnames(Control.Counts.Genera.filt))]
# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Control.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))
# check and make sure it looks right
TimePoints.Control.Genera
## [1] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [10] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [19] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [28] "Day0" "Day14" "Day7"
More than two conditions this time, use the ANOVA-like test, Kruskal Wallis
filt.Genera.Control.ByTime.aldex <- aldex(Control.Counts.Genera.filt,
TimePoints.Control.Genera,
mc.samples = 1000,
test = "kw",
effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode
We are looking at glm.eBH for the BH corrected ANOVA pvalue
filt.Genera.Control.ByTime.aldex <-
filt.Genera.Control.ByTime.aldex[order(filt.Genera.Control.ByTime.aldex$glm.eBH,
decreasing = FALSE),]
kable(head(filt.Genera.Control.ByTime.aldex))
| kw.ep | kw.eBH | glm.ep | glm.eBH | |
|---|---|---|---|---|
| Oribacterium | 0.0005354 | 0.1104829 | 0.0000000 | 0.0000158 |
| Streptococcus | 0.0000760 | 0.0558923 | 0.0000000 | 0.0000159 |
| Lactococcus | 0.0004159 | 0.1045718 | 0.0000065 | 0.0015823 |
| Granulicatella | 0.0007024 | 0.1276212 | 0.0000951 | 0.0149421 |
| T4-like viruses | 0.0056658 | 0.3088761 | 0.0011659 | 0.0822780 |
| Schizosaccharomyces | 0.0094127 | 0.3725284 | 0.0026917 | 0.1301247 |
hist(filt.Genera.Control.ByTime.aldex$glm.eBH,
breaks = 20,
main = "Histogram of p-values on the effect of time within the control diet on genera",
xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
How many significantly different genera are there?
filt.Genera.Control.ByTime.aldex.sig <-
filt.Genera.Control.ByTime.aldex[which(filt.Genera.Control.ByTime.aldex$glm.eBH<0.05),]
length(rownames(filt.Genera.Control.ByTime.aldex.sig))
## [1] 4
4 sig genera
Which genera are they?
sig_control_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Control.ByTime.aldex.sig),
filt.Genera.Control.ByTime.aldex.sig$glm.eBH))
sig_control_genera_aldex2 <- sig_control_genera_aldex2 %>%
rename(Genera = V1,
glm.eBH_pval = V2)
sig_control_genera_aldex2
## Genera glm.eBH_pval
## 1 Oribacterium 1.58068029273393e-05
## 2 Streptococcus 1.58746766478819e-05
## 3 Lactococcus 0.00158226394942239
## 4 Granulicatella 0.0149420529335598
# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Genera.filt <- genera_counts_foraldex %>%
select(contains("Tomato"))
ALDEx2 function needs a factor of variables
# results in pigs at different time points being grouped together
Tomato.Counts.Genera.filt <- Tomato.Counts.Genera.filt[order(colnames(Tomato.Counts.Genera.filt))]
# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Tomato.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))
# check and make sure it looks right
TimePoints.Tomato.Genera
## [1] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [10] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [19] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [28] "Day0" "Day14" "Day7"
More than two conditions this time, use the ANOVA-like test
filt.Genera.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Genera.filt,
TimePoints.Tomato.Genera,
mc.samples = 1000,
test = "kw",
effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode
We are looking at glm.eBH for the BH corrected ANOVA pvalue
filt.Genera.Tomato.ByTime.aldex <-
filt.Genera.Tomato.ByTime.aldex[order(filt.Genera.Tomato.ByTime.aldex$glm.eBH,
decreasing = FALSE),]
kable(head(filt.Genera.Tomato.ByTime.aldex))
| kw.ep | kw.eBH | glm.ep | glm.eBH | |
|---|---|---|---|---|
| Staphylococcus | 0.0000565 | 0.0342115 | 0.0000000 | 0.0000001 |
| Alphatorquevirus | 0.0001143 | 0.0390495 | 0.0000003 | 0.0000802 |
| Lambda-like viruses | 0.0002278 | 0.0582011 | 0.0000017 | 0.0004113 |
| unclassified (derived from Bacteria) | 0.0019396 | 0.2946385 | 0.0000066 | 0.0012508 |
| Streptococcus | 0.0025595 | 0.3275011 | 0.0014981 | 0.1707533 |
| Crocosphaera | 0.0117640 | 0.4694176 | 0.0104829 | 0.3025469 |
hist(filt.Genera.Tomato.ByTime.aldex$glm.eBH,
breaks = 20,
main = "Histogram of p-values on the effect of time within the tomato diet on genera",
xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
How many significantly different genera are there?
filt.Genera.Tomato.ByTime.aldex.sig <-
filt.Genera.Tomato.ByTime.aldex[which(filt.Genera.Tomato.ByTime.aldex$glm.eBH<0.05),]
length(rownames(filt.Genera.Tomato.ByTime.aldex.sig))
## [1] 4
4 sig genera
Which genera are they?
sig_tomato_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Tomato.ByTime.aldex.sig),
filt.Genera.Tomato.ByTime.aldex.sig$glm.eBH))
sig_tomato_genera_aldex2 <- sig_tomato_genera_aldex2 %>%
rename(Genera = V1,
glm.eBH_pval = V2)
sig_tomato_genera_aldex2
## Genera glm.eBH_pval
## 1 Staphylococcus 1.09222660374843e-07
## 2 Alphatorquevirus 8.01595357034658e-05
## 3 Lambda-like viruses 0.000411303309560209
## 4 unclassified (derived from Bacteria) 0.00125079010124612
Any overlap between sig differences at day 14 and by diet?
Control over time and day 14 overlap
intersect(sig_day14_genera_aldex2$Genera, sig_control_genera_aldex2$Genera)
## [1] "Streptococcus"
Streptococcus
Tomato over time and day 14 overlap
intersect(sig_day14_genera_aldex2$Genera, sig_tomato_genera_aldex2$Genera)
## [1] "Lambda-like viruses"
## [2] "Staphylococcus"
## [3] "Alphatorquevirus"
## [4] "unclassified (derived from Bacteria)"
Read in phyla level data, annotated from MG-RAST. In “Phyla” tab of Supplementary Information.
Phyla.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
sheet = "TableS3.Phyla")
str(Phyla.Counts)
## tibble [60 × 62] (S3: tbl_df/tbl/data.frame)
## $ domain : chr [1:60] "Bacteria" "Bacteria" "Eukaryota" "Bacteria" ...
## $ phylum : chr [1:60] "Acidobacteria" "Actinobacteria" "Apicomplexa" "Aquificae" ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:60] 2874 186789 231 1953 368 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day0 : num [1:60] 3717 277130 384 2254 992 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:60] 2663 126155 190 1642 386 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7 : num [1:60] 880 39557 168 647 211 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day7 : num [1:60] 2016 142345 171 1418 400 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7 : num [1:60] 1377 181295 101 658 201 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7 : num [1:60] 1570 58263 224 892 340 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:60] 1298 109273 287 900 211 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day0 : num [1:60] 3114 159425 529 2095 764 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0 : num [1:60] 2604 168472 170 1422 393 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:60] 3118 163425 231 1192 426 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day0 : num [1:60] 2796 70967 389 1137 605 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:60] 2222 91465 325 1561 364 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0 : num [1:60] 2136 68481 402 1377 691 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0 : num [1:60] 2017 207693 143 1265 361 ...
## $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:60] 836 26050 147 633 212 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:60] 2612 172091 181 1645 393 ...
## $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:60] 2136 122681 250 1536 445 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7 : num [1:60] 1090 78218 168 774 304 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:60] 2693 263950 266 1713 577 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day7 : num [1:60] 3420 101192 582 2369 766 ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day7 : num [1:60] 2216 159323 115 1383 303 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0 : num [1:60] 2146 78205 221 1265 390 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0 : num [1:60] 732 77377 292 585 223 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:60] 2079 142139 322 1335 392 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:60] 570 25927 180 425 270 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0 : num [1:60] 2472 82091 415 1534 647 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day0 : num [1:60] 1607 88397 432 1085 423 ...
## $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7 : num [1:60] 278 17451 96 150 107 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7 : num [1:60] 1100 56205 157 984 306 ...
## $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0 : num [1:60] 1562 74553 171 780 238 ...
## $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:60] 765 47957 182 551 237 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0 : num [1:60] 2182 124473 280 1483 476 ...
## $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:60] 3329 116448 325 2149 703 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day7 : num [1:60] 1920 55849 156 1234 408 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7 : num [1:60] 757 38904 204 583 317 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0 : num [1:60] 2037 120272 320 1399 561 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7 : num [1:60] 1279 87121 215 917 409 ...
## $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:60] 583 36948 69 444 102 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:60] 496 29179 99 374 142 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day0 : num [1:60] 2963 90535 278 1596 631 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:60] 2548 133556 181 1734 432 ...
## $ ShotgunWGS-ControlPig6GutMicrobiome-Day0 : num [1:60] 2269 127508 314 1058 413 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0 : num [1:60] 1935 110140 207 1018 425 ...
## $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:60] 817 33981 133 443 187 ...
## $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:60] 705 92977 122 507 148 ...
## $ ShotgunWGS-ControlPig7GutMicrobiome-Day7 : num [1:60] 1131 69602 297 628 290 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day7 : num [1:60] 1298 112714 203 983 325 ...
## $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:60] 566 36447 72 514 125 ...
## $ ShotgunWGS-ControlPig8GutMicrobiome-Day7 : num [1:60] 2173 159187 378 1311 361 ...
## $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:60] 1186 49134 150 858 249 ...
## $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:60] 1122 64744 254 1030 427 ...
## $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7 : num [1:60] 1109 97728 149 670 211 ...
## $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:60] 2350 83993 210 1719 446 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day0 : num [1:60] 3314 428097 206 2366 519 ...
## $ ShotgunWGS-ControlPig5GutMicrobiome-Day0 : num [1:60] 2998 242356 283 1895 758 ...
## $ ShotgunWGS-ControlPig4GutMicrobiome-Day0 : num [1:60] 3042 223010 351 1777 685 ...
## $ ShotgunWGS-ControlPig9GutMicrobiome-Day7 : num [1:60] 499 68424 784 329 171 ...
## $ ShotgunWGS-ControlPig3GutMicrobiome-Day7 : num [1:60] 2620 340300 165 1993 484 ...
## $ ShotgunWGS-TomatoPig17GutMicrobime-Day7 : num [1:60] 1340 71395 159 648 270 ...
These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.
Phyla.Counts.Filt <- Phyla.Counts %>%
filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" ,
phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
phylum != "Platyhelminthes")
Transpose.
Phyla.Counts.Filt.t <- as.tibble(t(Phyla.Counts.Filt))
# make phyla colnames
colnames(Phyla.Counts.Filt.t) <- Phyla.Counts.Filt.t[2,]
# remove domain, phylum rows
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t[3:62,]
# convert character to numeric
Phyla.Counts.Filt.t <- as.data.frame(apply((Phyla.Counts.Filt.t), 2, as.numeric))
str(Phyla.Counts.Filt.t[,1:5])
## 'data.frame': 60 obs. of 5 variables:
## $ Acidobacteria : num 2874 3717 2663 880 2016 ...
## $ Actinobacteria: num 186789 277130 126155 39557 142345 ...
## $ Apicomplexa : num 231 384 190 168 171 101 224 287 529 170 ...
## $ Aquificae : num 1953 2254 1642 647 1418 ...
## $ Ascomycota : num 1491 2196 1281 672 1178 ...
# add back sample names as column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
mutate(Sample_Name = AllSamples.Metadata$Sample_Name)
# move Sample_Name to first column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
relocate(Sample_Name)
kable(head(Phyla.Counts.Filt.t))
| Sample_Name | Acidobacteria | Actinobacteria | Apicomplexa | Aquificae | Ascomycota | Bacillariophyta | Bacteroidetes | Basidiomycota | Blastocladiomycota | Candidatus Poribacteria | Chlamydiae | Chlorobi | Chloroflexi | Chlorophyta | Chromerida | Chrysiogenetes | Chytridiomycota | Crenarchaeota | Cyanobacteria | Deferribacteres | Deinococcus-Thermus | Dictyoglomi | Elusimicrobia | Euglenida | Euryarchaeota | Fibrobacteres | Firmicutes | Fusobacteria | Gemmatimonadetes | Glomeromycota | Hemichordata | Korarchaeota | Lentisphaerae | Microsporidia | Nanoarchaeota | Nematoda | Nitrospirae | Phaeophyceae | Placozoa | Planctomycetes | Proteobacteria | Spirochaetes | Synergistetes | Tenericutes | Thaumarchaeota | Thermotogae | Verrucomicrobia | Xanthophyceae | unclassified (derived from Bacteria) | unclassified (derived from Eukaryota) | unclassified (derived from Fungi) | unclassified (derived from Viruses) | unclassified (derived from other sequences) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShotgunWGS-ControlPig6GutMicrobiome-Day14 | 2874 | 186789 | 231 | 1953 | 1491 | 105 | 1424565 | 240 | 0 | 26 | 552 | 4889 | 7842 | 370 | 0 | 331 | 0 | 648 | 8838 | 1494 | 2481 | 1217 | 632 | 3 | 13175 | 4768 | 2059948 | 15350 | 211 | 0 | 26 | 75 | 765 | 49 | 4 | 178 | 551 | 0 | 68 | 1523 | 105309 | 11519 | 4453 | 2764 | 56 | 5014 | 3209 | 1 | 1197 | 1260 | 0 | 1546 | 48 |
| ShotgunWGS-ControlPig8GutMicrobiome-Day0 | 3717 | 277130 | 384 | 2254 | 2196 | 184 | 1391417 | 405 | 0 | 49 | 829 | 6073 | 9612 | 571 | 0 | 416 | 0 | 902 | 13612 | 1994 | 3586 | 1554 | 1007 | 0 | 19176 | 6963 | 2223331 | 21242 | 340 | 0 | 18 | 97 | 1833 | 88 | 5 | 265 | 797 | 1 | 68 | 2632 | 154698 | 17463 | 7489 | 3731 | 80 | 7105 | 6282 | 1 | 1720 | 4189 | 1 | 2626 | 33 |
| ShotgunWGS-ControlPig3GutMicrobiome-Day14 | 2663 | 126155 | 190 | 1642 | 1281 | 129 | 1260217 | 198 | 0 | 21 | 554 | 4469 | 7596 | 362 | 0 | 271 | 0 | 638 | 11276 | 1419 | 2321 | 1109 | 699 | 0 | 12790 | 4985 | 2266610 | 14356 | 241 | 0 | 16 | 52 | 774 | 56 | 4 | 146 | 563 | 2 | 31 | 1570 | 104879 | 10922 | 4148 | 2493 | 59 | 5052 | 3738 | 0 | 1148 | 1266 | 0 | 2003 | 62 |
| ShotgunWGS-TomatoPig14GutMicrobiome-Day7 | 880 | 39557 | 168 | 647 | 672 | 65 | 415935 | 114 | 0 | 17 | 223 | 1565 | 2849 | 184 | 0 | 114 | 2 | 328 | 3426 | 543 | 967 | 424 | 359 | 1 | 8299 | 1750 | 628580 | 5545 | 61 | 0 | 5 | 36 | 492 | 24 | 6 | 138 | 241 | 1 | 19 | 540 | 71783 | 5634 | 1870 | 1292 | 37 | 1998 | 1259 | 0 | 1161 | 662 | 6 | 1010 | 115 |
| ShotgunWGS-ControlPig5GutMicrobiome-Day7 | 2016 | 142345 | 171 | 1418 | 1178 | 111 | 798569 | 182 | 1 | 22 | 505 | 3835 | 6249 | 388 | 0 | 273 | 0 | 652 | 10511 | 1166 | 2095 | 897 | 665 | 2 | 13289 | 4040 | 1919749 | 13260 | 211 | 0 | 8 | 66 | 1339 | 80 | 4 | 161 | 483 | 1 | 27 | 1674 | 114187 | 10757 | 4358 | 2309 | 55 | 4527 | 3442 | 1 | 1323 | 1369 | 0 | 1475 | 13 |
| ShotgunWGS-TomatoPig18GutMicrobiome-Day7 | 1377 | 181295 | 101 | 658 | 799 | 64 | 690378 | 119 | 0 | 17 | 222 | 2198 | 3255 | 220 | 0 | 91 | 0 | 274 | 7460 | 445 | 1102 | 423 | 230 | 0 | 4970 | 2071 | 793943 | 5068 | 208 | 0 | 2 | 25 | 288 | 32 | 1 | 134 | 245 | 0 | 29 | 1096 | 69391 | 4728 | 1482 | 915 | 26 | 1710 | 2361 | 0 | 1431 | 518 | 0 | 1267 | 59 |
Calculate relative abundance, and bind back to metadata.
Phyla.Counts.Filt.t.wtotal <- Phyla.Counts.Filt.t %>%
mutate(Total.Counts = rowSums(Phyla.Counts.Filt.t[,2:ncol(Phyla.Counts.Filt.t)]))
dim(Phyla.Counts.Filt.t.wtotal)
## [1] 60 55
# create rel abund df
RelAbund.Phyla.Filt <- Phyla.Counts.Filt.t.wtotal[,2:54]/Phyla.Counts.Filt.t.wtotal$Total.Counts
# add back metadata
RelAbund.Phyla.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Phyla.Filt)
# remove metadata
RelAbund.Phyla.Filt.nometadata <- RelAbund.Phyla.Filt %>%
select_if(is.numeric)
# create a list with the number of zeros for each genus
counting_zeros_phyla <- sapply(RelAbund.Phyla.Filt.nometadata, function(x){ (sum(x==0))})
# plot a histogram to look
counting_zeros_phyla_df <- as.data.frame(counting_zeros_phyla)
hist(counting_zeros_phyla_df$counting_zeros_phyla,
breaks = 61,
main = "Histogram of Genera with Zero Relative Intensity",
sub = "Starting at No Zeros",
xlab = "Number of zero relative intensity values",
ylab = "Frequency")
Big first bar is many phyla which have zero missing values.
# filter for any phyla with at least 1 missing value
counting_zeros_phyla_df_missingval <- counting_zeros_phyla_df %>%
rownames_to_column(var = "rowname") %>%
filter(counting_zeros_phyla > 0) %>%
column_to_rownames(var = "rowname")
# how many genera have at least one missing value?
dim(counting_zeros_phyla_df_missingval)
## [1] 9 1
9 phyla have at least 1 missing value.
# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_phyla_df_missingval$counting_zeros_phyla,
breaks = 60,
main = "Histogram of Genera with Zero Relative Intensity",
sub = "Starting at 1 Zero",
xlab = "Number of zero relative intensity values",
ylab = "Frequency")
# create table of number of phyla with more than 1 missing value
counting_zeros_phyla_df_missingval
## counting_zeros_phyla
## Blastocladiomycota 55
## Chromerida 48
## Chytridiomycota 42
## Euglenida 31
## Glomeromycota 59
## Nanoarchaeota 8
## Phaeophyceae 35
## Xanthophyceae 29
## unclassified (derived from Fungi) 46
This would mean 33% missing values in our dataset.
# removing phyla that have 20 or more zeros
counting_zeros_phyla_df_missing20ormore <- counting_zeros_phyla_df %>%
rownames_to_column(var = "rowname") %>%
filter(counting_zeros_phyla >= 20) %>%
column_to_rownames(var = "rowname")
# how many phyla have 20 or more missing value?
dim(counting_zeros_phyla_df_missing20ormore)
## [1] 8 1
8 phyla have more than 20 missing values.
# make a character vector from the rownames of previous data frame containing the phyla we want to get rid of
phyla.20zeros <- c(rownames(counting_zeros_phyla_df_missing20ormore))
# use select function to select all columns EXCEPT the ones in the character vector, we want to remove those
# and add in metadata
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt %>%
select(everything(), -all_of(phyla.20zeros))
# check dimensions to make sure it filtered correctly
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
# removed 8, like we expected
Our final dataset has 45 phyla (because 5 columns are metadata).
Write final dataset genus rel abund to csv
write_csv(RelAbund.Phyla.Filt.zerofilt,
file = "Phyla_RelAbund_Final_Filtered_WithMetadata.csv")
See “Genera” section above for rarefaction curves and kronas plots
Wrangling to enable collection of some summary statistics about our microbiome profile.
Grab names of final phyla
# contains inplausible genera removed, but not removed for zeroes
dim(Phyla.Counts.Filt)
## [1] 53 62
Phyla.Counts.Filt[1:10, 1:5]
## # A tibble: 10 × 5
## domain phylum `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-Co…`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Bacteria Acidobacteria 2874 3717 2663
## 2 Bacteria Actinobacteria 186789 277130 126155
## 3 Eukaryota Apicomplexa 231 384 190
## 4 Bacteria Aquificae 1953 2254 1642
## 5 Eukaryota Ascomycota 1491 2196 1281
## 6 Eukaryota Bacillariophyta 105 184 129
## 7 Bacteria Bacteroidetes 1424565 1391417 1260217
## 8 Eukaryota Basidiomycota 240 405 198
## 9 Eukaryota Blastocladiomyc… 0 0 0
## 10 Bacteria Candidatus Pori… 26 49 21
# final filtered data
RelAbund.Phyla.Filt.zerofilt[1:10, 1:5]
## # A tibble: 10 × 5
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po…
## <chr> <fct> <fct> <fct> <fct>
## 1 ShotgunWGS-ControlPig6GutMicrobiome-… 6 Cont… Day 14 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-… 8 Cont… Day 0 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-… 3 Cont… Day 14 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-… 14 Toma… Day 7 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-… 5 Cont… Day 7 Control Day 7
## 6 ShotgunWGS-TomatoPig18GutMicrobiome-… 18 Toma… Day 7 Tomato Day 7
## 7 ShotgunWGS-TomatoPig16GutMicrobiome-… 16 Toma… Day 7 Tomato Day 7
## 8 ShotgunWGS-ControlPig10GutMicrobiome… 10 Cont… Day 7 Control Day 7
## 9 ShotgunWGS-ControlPig2GutMicrobiome-… 2 Cont… Day 0 Control Day 0
## 10 ShotgunWGS-TomatoPig18GutMicrobiome-… 18 Toma… Day 0 Tomato Day 0
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
# grab colnames which have all the final phyla
final_phyla <- colnames(RelAbund.Phyla.Filt.zerofilt)
final_phyla
## [1] "Sample_Name"
## [2] "Pig"
## [3] "Diet"
## [4] "Time_Point"
## [5] "Diet_By_Time_Point"
## [6] "Acidobacteria"
## [7] "Actinobacteria"
## [8] "Apicomplexa"
## [9] "Aquificae"
## [10] "Ascomycota"
## [11] "Bacillariophyta"
## [12] "Bacteroidetes"
## [13] "Basidiomycota"
## [14] "Candidatus Poribacteria"
## [15] "Chlamydiae"
## [16] "Chlorobi"
## [17] "Chloroflexi"
## [18] "Chlorophyta"
## [19] "Chrysiogenetes"
## [20] "Crenarchaeota"
## [21] "Cyanobacteria"
## [22] "Deferribacteres"
## [23] "Deinococcus-Thermus"
## [24] "Dictyoglomi"
## [25] "Elusimicrobia"
## [26] "Euryarchaeota"
## [27] "Fibrobacteres"
## [28] "Firmicutes"
## [29] "Fusobacteria"
## [30] "Gemmatimonadetes"
## [31] "Hemichordata"
## [32] "Korarchaeota"
## [33] "Lentisphaerae"
## [34] "Microsporidia"
## [35] "Nanoarchaeota"
## [36] "Nematoda"
## [37] "Nitrospirae"
## [38] "Placozoa"
## [39] "Planctomycetes"
## [40] "Proteobacteria"
## [41] "Spirochaetes"
## [42] "Synergistetes"
## [43] "Tenericutes"
## [44] "Thaumarchaeota"
## [45] "Thermotogae"
## [46] "Verrucomicrobia"
## [47] "unclassified (derived from Bacteria)"
## [48] "unclassified (derived from Eukaryota)"
## [49] "unclassified (derived from Viruses)"
## [50] "unclassified (derived from other sequences)"
# remove metadata colnames
final_phyla <- final_phyla[6:50]
final_phyla <- as.data.frame(final_phyla)
final_phyla <- final_phyla %>%
rename(phylum = final_phyla)
Get back domain and inner_join with final_phyla list
# pull from full dataset the domain and genus columns
Phyla.Counts.Filt.Domain.Phyla <- Phyla.Counts.Filt %>%
select(domain, phylum)
head(Phyla.Counts.Filt.Domain.Phyla)
## # A tibble: 6 × 2
## domain phylum
## <chr> <chr>
## 1 Bacteria Acidobacteria
## 2 Bacteria Actinobacteria
## 3 Eukaryota Apicomplexa
## 4 Bacteria Aquificae
## 5 Eukaryota Ascomycota
## 6 Eukaryota Bacillariophyta
# want to join Genus.Counts.Filt.Domain.Genera with final_phyla
final_phyla_withdomain <- inner_join(final_phyla, Phyla.Counts.Filt.Domain.Phyla,
by = "phylum")
final_phyla_withdomain %>%
count()
## n
## 1 45
final_phyla_withdomain %>%
group_by(domain) %>%
count()
## # A tibble: 5 × 2
## # Groups: domain [5]
## domain n
## <chr> <int>
## 1 Archaea 5
## 2 Bacteria 28
## 3 Eukaryota 10
## 4 other sequences 1
## 5 Viruses 1
RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]
## # A tibble: 5 × 10
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… Acidobacteria
## <chr> <fct> <fct> <fct> <fct> <dbl>
## 1 ShotgunWGS-ControlPig6G… 6 Cont… Day 14 Control Day 14 0.000741
## 2 ShotgunWGS-ControlPig8G… 8 Cont… Day 0 Control Day 0 0.000885
## 3 ShotgunWGS-ControlPig3G… 3 Cont… Day 14 Control Day 14 0.000690
## 4 ShotgunWGS-TomatoPig14G… 14 Toma… Day 7 Tomato Day 7 0.000732
## 5 ShotgunWGS-ControlPig5G… 5 Cont… Day 7 Control Day 7 0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## # Aquificae <dbl>, Ascomycota <dbl>
phyla_means <- RelAbund.Phyla.Filt.zerofilt %>%
summarize_if(is.numeric, mean)
phyla_means_t <- t(phyla_means)
phyla_means_t <- as.data.frame(phyla_means_t)
phyla_means_t %>%
rename(rel_abund_phyla = V1) %>%
arrange(-rel_abund_phyla)
## rel_abund_phyla
## Firmicutes 5.273546e-01
## Bacteroidetes 3.544950e-01
## Actinobacteria 4.660132e-02
## Proteobacteria 3.859541e-02
## Fusobacteria 4.300028e-03
## Euryarchaeota 4.280344e-03
## Spirochaetes 3.786741e-03
## Cyanobacteria 2.973060e-03
## Chloroflexi 2.143931e-03
## Fibrobacteres 1.647436e-03
## Thermotogae 1.475999e-03
## Synergistetes 1.429229e-03
## Chlorobi 1.371726e-03
## Verrucomicrobia 1.113246e-03
## Tenericutes 8.325538e-04
## unclassified (derived from Viruses) 7.714176e-04
## Acidobacteria 7.491839e-04
## Deinococcus-Thermus 7.206358e-04
## unclassified (derived from Eukaryota) 5.727411e-04
## unclassified (derived from Bacteria) 5.606341e-04
## Ascomycota 5.224676e-04
## Planctomycetes 5.021699e-04
## Aquificae 4.911229e-04
## Deferribacteres 4.032208e-04
## Lentisphaerae 3.300053e-04
## Chlamydiae 3.262732e-04
## Dictyoglomi 3.130261e-04
## Elusimicrobia 2.375048e-04
## Crenarchaeota 2.006612e-04
## Nitrospirae 1.656603e-04
## Chlorophyta 1.318528e-04
## Apicomplexa 1.227757e-04
## Basidiomycota 8.845319e-05
## Nematoda 8.275259e-05
## Chrysiogenetes 8.110410e-05
## Gemmatimonadetes 6.650590e-05
## Bacillariophyta 3.943884e-05
## unclassified (derived from other sequences) 2.783963e-05
## Microsporidia 2.109075e-05
## Thaumarchaeota 1.895141e-05
## Korarchaeota 1.880226e-05
## Placozoa 1.426135e-05
## Candidatus Poribacteria 1.034724e-05
## Hemichordata 4.906748e-06
## Nanoarchaeota 1.627075e-06
The most prevalent phyla are Firmicutes (52.7% average abundance), Bacteroidetes (35.4%), Actinobacteria (4.7%), Proteobacteria (3.9%) and Fusobaceria (0.43%).
What is the standard deviation of phyla with the highest relative abundance?
RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]
## # A tibble: 5 × 10
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… Acidobacteria
## <chr> <fct> <fct> <fct> <fct> <dbl>
## 1 ShotgunWGS-ControlPig6G… 6 Cont… Day 14 Control Day 14 0.000741
## 2 ShotgunWGS-ControlPig8G… 8 Cont… Day 0 Control Day 0 0.000885
## 3 ShotgunWGS-ControlPig3G… 3 Cont… Day 14 Control Day 14 0.000690
## 4 ShotgunWGS-TomatoPig14G… 14 Toma… Day 7 Tomato Day 7 0.000732
## 5 ShotgunWGS-ControlPig5G… 5 Cont… Day 7 Control Day 7 0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## # Aquificae <dbl>, Ascomycota <dbl>
phyla_sd <- RelAbund.Phyla.Filt.zerofilt %>%
summarize_if(is.numeric, sd)
phyla_sd_t <- t(phyla_sd)
phyla_sd_t <- as.data.frame(phyla_sd_t)
phyla_sd_t <- phyla_sd_t %>%
rename(sd_phyla = V1) %>%
arrange(-sd_phyla)
head(phyla_sd_t)
## sd_phyla
## Bacteroidetes 0.059401369
## Firmicutes 0.055579086
## Actinobacteria 0.018163420
## Proteobacteria 0.012837716
## Euryarchaeota 0.001445649
## Cyanobacteria 0.001357954
The standard deviations of most prevalent phyla are Firmicutes (5.5% average abundance), Bacteroidetes (5.9%), Actinobacteria (1.8%), Proteobacteria (1.2%) and Fusobaceria (8.5 x 10^4%).
What percent of the reads are from Bacteria?
final_phyla_bacteriaonly <- final_phyla_withdomain %>%
filter(domain == "Bacteria")
final_phyla_bacteriaonly <- final_phyla_bacteriaonly$phylum
# select columns corresponding to bacteria
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt %>%
select(contains(final_phyla_bacteriaonly))
# create rowsums
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt.baconly %>%
mutate(rowsums = rowSums(RelAbund.Phyla.Filt.zerofilt.baconly[]))
mean(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)
## [1] 0.9930777
sd(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)
## [1] 0.002045929
Repeated measures, using Pig as a block and set permutations using how() ORIGINAL BLOCK
set.seed(2021)
# create factors
factors_time_diet_pig_phyla <- RelAbund.Phyla.Filt.zerofilt %>%
select(Time_Point, Diet, Pig)
# create permutations
perm_time_diet_pig_phyla <- how(nperm = 9999)
setBlocks(perm_time_diet_pig_phyla) <- with(factors_time_diet_pig_phyla, Pig)
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
data = factors_time_diet_pig_phyla,
permutations = perm_time_diet_pig_phyla,
method = "bray")
AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks: with(factors_time_diet_pig_phyla, Pig)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.007675 0.02656 1.6746 0.0136 *
## Time_Point 2 0.028496 0.09860 3.1087 0.0046 **
## Diet:Time_Point 2 0.005338 0.01847 0.5824 0.4915
## Residual 54 0.247497 0.85637
## Total 59 0.289007 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Interaction:
# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)
# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=Pig, type="free",))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
data = factors_time_diet_pig_phyla,
permutations = perm_time_diet_pig_phyla,
method = "bray",
by = "margin")
AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Diet:Time_Point 2 0.005338 0.01847 0.5824 0.515
## Residual 54 0.247497 0.85637
## Total 59 0.289007 1.00000
Interaction not significant (p=.51), so remove from model
# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)
# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=Pig, type = "free"))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet + Time_Point,
data = factors_time_diet_pig_phyla,
permutations = perm_time_diet_pig_phyla,
method = "bray",
by = "margin")
AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet + Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.007675 0.02656 1.7000 0.295
## Time_Point 2 0.028496 0.09860 3.1558 0.015 *
## Residual 56 0.252835 0.87484
## Total 59 0.289007 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Test for homogeneity of multivariate dispersions
dis <- vegdist(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, Diet)
permutest(mod)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.000153 0.00015277 0.121 999 0.726
## Residuals 58 0.073213 0.00126230
Non significant! good for our PERMANOVA test validity
Effect of control diet over time.
# filter data set for only control samples
control.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Control")
# create factors
factors_control_pig_phyla <- droplevels(control.RelAbund.Phyla.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_pig_phyla$Pig, type = "free"))
# run PERMANOVA
Control.ByTime.Phyla.zerofilt.permanova <- adonis2(control.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_pig_phyla,
permutations = perm_control_pig_phyla,
method = "bray",
by = "margin")
Control.ByTime.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_pig_phyla, permutations = perm_control_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 2 0.025943 0.17486 2.8609 0.01 **
## Residual 27 0.122422 0.82514
## Total 29 0.148365 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Significant effect of time (p = 0.005) within control samples. Beta diversity changing with time. Now the question is where is the difference coming from (ie. between which time points?)
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 14")
# create factors
factors_control_T1T2_pig_phyla <- droplevels(control.T1T2.RelAbund.Phyla.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T1T2_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T1T2_pig_phyla$Pig,
type = "free"))
# run PERMANOVA
Control.T1T2.Phyla.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T1T2_pig_phyla,
permutations = perm_control_T1T2_pig_phyla,
method = "bray",
by = "margin")
Control.T1T2.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T2_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T1T2.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T2_pig_phyla, permutations = perm_control_T1T2_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.01404 0.11986 2.4513 0.03 *
## Residual 18 0.10309 0.88014
## Total 19 0.11713 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
p=.085 so not significant between T1 and T2
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 7")
# create factors
factors_control_T1T3_pig_phyla <- droplevels(control.T1T3.RelAbund.Phyla.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T1T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T1T3_pig_phyla$Pig,
type = "free"))
# run PERMANOVA
Control.T1T3.Phyla.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T1T3_pig_phyla,
permutations = perm_control_T1T3_pig_phyla,
method = "bray",
by = "margin")
Control.T1T3.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T3_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T1T3.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T3_pig_phyla, permutations = perm_control_T1T3_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.022462 0.28421 7.1469 0.02 *
## Residual 18 0.056572 0.71579
## Total 19 0.079034 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
P = .02 so significant. There is a significant difference between T1 and T3 in the control diet pigs
# filter data set for only samples at T2 and T3
control.T2T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 0")
# create factors
factors_control_T2T3_pig_phyla <- droplevels(control.T2T3.RelAbund.Phyla.zerofilt %>%
select(Time_Point, Pig))
# create permutations
perm_control_T2T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_control_T2T3_pig_phyla$Pig,
type = "free"))
# run PERMANOVA
Control.T2T3.Phyla.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
data = factors_control_T2T3_pig_phyla,
permutations = perm_control_T2T3_pig_phyla,
method = "bray",
by = "margin")
Control.T2T3.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T2T3_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = control.T2T3.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T2T3_pig_phyla, permutations = perm_control_T2T3_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 1 0.002413 0.02755 0.51 0.33
## Residual 18 0.085178 0.97245
## Total 19 0.087591 1.00000
P = .315 so not significant
Effect of tomato diet over time.
# filter data for only tomato samples
tomato.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Tomato")
# create factors
factors_tomato_pig_phyla <- tomato.RelAbund.Phyla.zerofilt %>%
select(Time_Point, Pig)
# create permutations
perm_tomato_pig_phyla <- how(within = Within(type="series", constant=TRUE),
plots = Plots(strata=factors_tomato_pig_phyla$Pig, type = "free"))
# run PERMANOVA
tomato.ByTime.Phyla.zerofilt.permanova <- adonis2(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
data = factors_tomato_pig_phyla,
permutations = perm_tomato_pig_phyla,
method = "bray",
by = "margin")
tomato.ByTime.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
##
## adonis2(formula = tomato.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_pig_phyla, permutations = perm_tomato_pig_phyla, method = "bray", by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## Time_Point 2 0.007891 0.05935 0.8517 0.34
## Residual 27 0.125075 0.94065
## Total 29 0.132966 1.00000
Non-significant effect of time (p = 0.325) within tomato samples. So no post hoc tests necessary.
Effect of diet at day 0.
# filter data set for only day 0 samples
d0.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 0")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day0_phyla <- d0.RelAbund.Phyla.zerofilt %>%
select(Diet)
# create permutations
perm_day0_phyla <- how(nperm = 9999)
# run PERMANOVA
d0.Phyla.zerofilt.permanova <- adonis2(d0.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
data = factors_day0_phyla,
permutations = perm_day0_phyla,
method = "bray")
d0.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d0.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day0_phyla, permutations = perm_day0_phyla, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.003515 0.04898 0.927 0.3692
## Residual 18 0.068249 0.95102
## Total 19 0.071764 1.00000
Non-significant effect of diet (p=0.376) at day 0.
Effect of diet at day 7.
# filter data set for only day 7 samples
d7.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 7")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day7_phyla <- d7.RelAbund.Phyla.zerofilt %>%
select(Diet)
# create permutations
perm_day7_phyla <- how(nperm = 9999)
# run PERMANOVA
d7.Phyla.zerofilt.permanova <- adonis2(d7.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
data = factors_day7_phyla,
permutations = perm_day7_phyla,
method = "bray")
d7.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d7.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day7_phyla, permutations = perm_day7_phyla, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.005267 0.04205 0.7901 0.4009
## Residual 18 0.119990 0.95795
## Total 19 0.125257 1.00000
Non-significant effect of diet (p=0.4097) at day 7.
Effect of diet at day 14.
# filter data set for only day 14 samples
d14.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 14")
# create factors
# don't need to include pig, since no repeated measures here
# only testing Diet within a time point
factors_day14_phyla <- d14.RelAbund.Phyla.zerofilt %>%
select(Diet)
# create permutations
perm_day14_phyla <- how(nperm = 9999)
# run PERMANOVA
d14.Phyla.zerofilt.permanova <- adonis2(d14.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
data = factors_day14_phyla,
permutations = perm_day14_phyla,
method = "bray")
d14.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## adonis2(formula = d14.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day14_phyla, permutations = perm_day14_phyla, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Diet 1 0.004232 0.06665 1.2854 0.2691
## Residual 18 0.059258 0.93335
## Total 19 0.063490 1.00000
Non-significant effect of diet (p=0.256) at day 14.
# calculate distances
phyla.filt.dist.zeros <- vegdist(RelAbund.Phyla.Filt.zerofilt[6:ncol(RelAbund.Phyla.Filt.zerofilt)],
method = "bray")
# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.phyla.filt.zerofilt <- cmdscale(phyla.filt.dist.zeros, k=2)
# make into data frame and bind metadata
scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(scale.phyla.filt.zerofilt, AllSamples.Metadata))
# do PCoA again, but get eigen values
scale.phyla.filt.zerofilt.eig <- cmdscale(phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
eigs.phyla.filt.zerofilt <- (100*((scale.phyla.filt.zerofilt.eig$eig)/(sum(scale.phyla.filt.zerofilt.eig$eig))))
# round the converted eigenvalues
round.eigs.phyla.filt.zerofilt <- round(eigs.phyla.filt.zerofilt, 3)
Plot
PCoA_phyla_20zeros_allsamples <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_classic() +
theme(axis.text = element_text(color = "black"))+
labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level")
PCoA_phyla_20zeros_allsamples
ggsave("Figures/BetaDiversity_PCoA_Phyla_allsamples.png",
plot = PCoA_phyla_20zeros_allsamples,
dpi = 800,
width = 10,
height = 8)
Re-level factors
scale.phyla.filt.zerofilt.df <- scale.phyla.filt.zerofilt.df %>%
mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
PCoA_phyla_20zeros_facetbytime <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_bw() +
theme(axis.text = element_text(color = "black"),
strip.background =element_rect(fill="white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Subset by Time Point") +
facet_wrap(~Time_Point)
PCoA_phyla_20zeros_facetbytime
ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByTimePoint.png",
plot = PCoA_phyla_20zeros_facetbytime,
dpi = 800,
width = 10,
height = 6)
PCoA_phyla_20zeros_facetbydiet <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
theme_bw() +
theme(axis.text = element_text(color = "black"),
strip.background =element_rect(fill="white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"),
fill="Diet & Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Subset by Diet") +
facet_wrap(~Diet)
PCoA_phyla_20zeros_facetbydiet
ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByDiet.png",
plot = PCoA_phyla_20zeros_facetbydiet,
dpi = 800,
width = 10,
height = 8)
Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different.
# calculate distances
control.phyla.filt.dist.zeros <- vegdist(control.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
control.scale.phyla.filt.zerofilt <- cmdscale(control.phyla.filt.dist.zeros, k=2)
# filter meta data
meta.control <- subset(AllSamples.Metadata, Diet == "Control")
# make pcoa table into data frame and bind metadata to it
control.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.control, control.scale.phyla.filt.zerofilt))
# do PCoA again, but get eigenvalues
control.scale.phyla.filt.zerofilt.eig <- cmdscale(control.phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
control.eigs.phyla.filt.zerofilt <- (100*((control.scale.phyla.filt.zerofilt.eig$eig)/sum(control.scale.phyla.filt.zerofilt.eig$eig)))
# round the eigenvalues
round.control.eigs.phyla.filt.zerofilt <- round(control.eigs.phyla.filt.zerofilt, 3)
Re-level factors
control.scale.phyla.filt.zerofilt.df$Time_Point <- factor(control.scale.phyla.filt.zerofilt.df$Time_Point,
levels = c("Day 0", "Day 7", "Day 14"))
Plot
control.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=paste("PC1: ", round.control.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.control.eigs.phyla.filt.zerofilt[2], "%"),
fill="Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Control Only")
# calculate distances
tomato.phyla.filt.dist.zeros <- vegdist(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
tomato.scale.phyla.filt.zerofilt <- cmdscale(tomato.phyla.filt.dist.zeros, k=2)
# filter meta data
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")
# make pcoa table into data frame and bind metadata to it
tomato.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.tomato, tomato.scale.phyla.filt.zerofilt))
# do PCoA again, but get eigenvalues
tomato.scale.phyla.filt.zerofilt.eig <- cmdscale(tomato.phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
tomato.eigs.phyla.filt.zerofilt <- (100*((tomato.scale.phyla.filt.zerofilt.eig$eig)/sum(tomato.scale.phyla.filt.zerofilt.eig$eig)))
# round the eigenvalues
round.tomato.eigs.phyla.filt.zerofilt <- round(tomato.eigs.phyla.filt.zerofilt, 3)
Re-level factors
tomato.scale.phyla.filt.zerofilt.df$Time_Point <- factor(tomato.scale.phyla.filt.zerofilt.df$Time_Point,
levels = c("Day 0", "Day 7", "Day 14"))
Plot
tomato.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("sienna1","firebrick3","tomato4")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=paste("PC1: ", round.tomato.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.tomato.eigs.phyla.filt.zerofilt[2], "%"),
fill="Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Tomato Only")
# calculate distances
d0.phyla.filt.dist.zeros <- vegdist(d0.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d0.scale.phyla.filt.zerofilt <- cmdscale(d0.phyla.filt.dist.zeros, k=2)
# filter meta data
meta.d0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")
# make pcoa table into data frame and bind metadata to it
d0.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d0, d0.scale.phyla.filt.zerofilt))
# do PCoA again, but get eigenvalues
d0.scale.phyla.filt.zerofilt.eig <- cmdscale(d0.phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
d0.eigs.phyla.filt.zerofilt <- (100*((d0.scale.phyla.filt.zerofilt.eig$eig)/sum(d0.scale.phyla.filt.zerofilt.eig$eig)))
# round the eigenvalues
round.d0.eigs.phyla.filt.zerofilt <- round(d0.eigs.phyla.filt.zerofilt, 3)
Plot
d0.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=paste("PC1: ", round.d0.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.d0.eigs.phyla.filt.zerofilt[2], "%"),
fill="Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Day 0 Only")
# calculate distances
d7.phyla.filt.dist.zeros <- vegdist(d7.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d7.scale.phyla.filt.zerofilt <- cmdscale(d7.phyla.filt.dist.zeros, k=2)
# filter meta data
meta.d7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")
# make pcoa table into data frame and bind metadata to it
d7.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d7, d7.scale.phyla.filt.zerofilt))
# do PCoA again, but get eigenvalues
d7.scale.phyla.filt.zerofilt.eig <- cmdscale(d7.phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
d7.eigs.phyla.filt.zerofilt <- (100*((d7.scale.phyla.filt.zerofilt.eig$eig)/sum(d7.scale.phyla.filt.zerofilt.eig$eig)))
# round the eigenvalues
round.d7.eigs.phyla.filt.zerofilt <- round(d7.eigs.phyla.filt.zerofilt, 3)
Plot
d7.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=paste("PC1: ", round.d7.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.d7.eigs.phyla.filt.zerofilt[2], "%"),
fill="Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Day 7 Only")
# calculate distances
d14.phyla.filt.dist.zeros <- vegdist(d14.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d14.scale.phyla.filt.zerofilt <- cmdscale(d14.phyla.filt.dist.zeros, k=2)
# filter meta data
meta.d14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")
# make pcoa table into data frame and bind metadata to it
d14.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d14, d14.scale.phyla.filt.zerofilt))
# do PCoA again, but get eigenvalues
d14.scale.phyla.filt.zerofilt.eig <- cmdscale(d14.phyla.filt.dist.zeros, k=2, eig = TRUE)
# convert eigenvalues to percentages and assign to a variable
d14.eigs.phyla.filt.zerofilt <- (100*((d14.scale.phyla.filt.zerofilt.eig$eig)/sum(d14.scale.phyla.filt.zerofilt.eig$eig)))
# round the eigenvalues
round.d14.eigs.phyla.filt.zerofilt <- round(d14.eigs.phyla.filt.zerofilt, 3)
Plot
d14.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
scale_fill_manual(values=c("steelblue2", "tomato2")) +
theme_classic() +
theme(axis.text = element_text(color = "black")) +
labs(x=paste("PC1: ", round.d14.eigs.phyla.filt.zerofilt[1], "%"),
y=paste("PC2: ", round.d14.eigs.phyla.filt.zerofilt[2], "%"),
fill="Time Point",
title = "Beta Diversity",
subtitle = "Phyla Level, Day 14 Only")
Given a priori interest in the phyla Bacteroidota/Bacteriodetes and Bacilotta/Firmicutes, we are conducted repeated measures ANOVA analysis for their changes in our samples. The ratio of Bacteroidota to Bacilotta is a commonly used metric for assessing the health of the microbiome, with a higher Bacteroidota to Bacilotta (formerly B to F) ratio being more beneficial.
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
60 samples, and 45 phyla (5 columns are metadata).
Re-level Time_Point
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt.zerofilt %>%
mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
levels(RelAbund.Phyla.Filt.zerofilt$Time_Point)
## [1] "Day 0" "Day 7" "Day 14"
Add column Other_phyla with the sum of all phyla that are not Bacteroidetes or Firmicutes
RelAbund.Phyla.Filt.zerofilt.withother <- RelAbund.Phyla.Filt.zerofilt %>%
mutate(Other_phyla = rowSums(select(.[6:ncol(.)], !contains(c("Bacteroidetes", "Firmicutes")))))
kable(head(RelAbund.Phyla.Filt.zerofilt.withother))
| Sample_Name | Pig | Diet | Time_Point | Diet_By_Time_Point | Acidobacteria | Actinobacteria | Apicomplexa | Aquificae | Ascomycota | Bacillariophyta | Bacteroidetes | Basidiomycota | Candidatus Poribacteria | Chlamydiae | Chlorobi | Chloroflexi | Chlorophyta | Chrysiogenetes | Crenarchaeota | Cyanobacteria | Deferribacteres | Deinococcus-Thermus | Dictyoglomi | Elusimicrobia | Euryarchaeota | Fibrobacteres | Firmicutes | Fusobacteria | Gemmatimonadetes | Hemichordata | Korarchaeota | Lentisphaerae | Microsporidia | Nanoarchaeota | Nematoda | Nitrospirae | Placozoa | Planctomycetes | Proteobacteria | Spirochaetes | Synergistetes | Tenericutes | Thaumarchaeota | Thermotogae | Verrucomicrobia | unclassified (derived from Bacteria) | unclassified (derived from Eukaryota) | unclassified (derived from Viruses) | unclassified (derived from other sequences) | Other_phyla |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShotgunWGS-ControlPig6GutMicrobiome-Day14 | 6 | Control | Day 14 | Control Day 14 | 0.0007406 | 0.0481336 | 0.0000595 | 0.0005033 | 0.0003842 | 2.71e-05 | 0.3670956 | 6.18e-05 | 6.70e-06 | 0.0001422 | 0.0012598 | 0.0020208 | 0.0000953 | 8.53e-05 | 0.0001670 | 0.0022775 | 0.0003850 | 0.0006393 | 0.0003136 | 0.0001629 | 0.0033951 | 0.0012287 | 0.5308271 | 0.0039555 | 0.0000544 | 6.7e-06 | 1.93e-05 | 0.0001971 | 1.26e-05 | 1.0e-06 | 0.0000459 | 0.0001420 | 1.75e-05 | 0.0003925 | 0.0271370 | 0.0029683 | 0.0011475 | 0.0007123 | 1.44e-05 | 0.0012921 | 0.0008269 | 0.0003085 | 0.0003247 | 0.0003984 | 1.24e-05 | 0.1020763 |
| ShotgunWGS-ControlPig8GutMicrobiome-Day0 | 8 | Control | Day 0 | Control Day 0 | 0.0008850 | 0.0659807 | 0.0000914 | 0.0005366 | 0.0005228 | 4.38e-05 | 0.3312767 | 9.64e-05 | 1.17e-05 | 0.0001974 | 0.0014459 | 0.0022885 | 0.0001359 | 9.90e-05 | 0.0002148 | 0.0032408 | 0.0004747 | 0.0008538 | 0.0003700 | 0.0002398 | 0.0045655 | 0.0016578 | 0.5293436 | 0.0050574 | 0.0000809 | 4.3e-06 | 2.31e-05 | 0.0004364 | 2.10e-05 | 1.2e-06 | 0.0000631 | 0.0001898 | 1.62e-05 | 0.0006266 | 0.0368314 | 0.0041577 | 0.0017830 | 0.0008883 | 1.90e-05 | 0.0016916 | 0.0014957 | 0.0004095 | 0.0009973 | 0.0006252 | 7.90e-06 | 0.1393790 |
| ShotgunWGS-ControlPig3GutMicrobiome-Day14 | 3 | Control | Day 14 | Control Day 14 | 0.0006897 | 0.0326727 | 0.0000492 | 0.0004253 | 0.0003318 | 3.34e-05 | 0.3263817 | 5.13e-05 | 5.40e-06 | 0.0001435 | 0.0011574 | 0.0019673 | 0.0000938 | 7.02e-05 | 0.0001652 | 0.0029204 | 0.0003675 | 0.0006011 | 0.0002872 | 0.0001810 | 0.0033125 | 0.0012911 | 0.5870258 | 0.0037180 | 0.0000624 | 4.1e-06 | 1.35e-05 | 0.0002005 | 1.45e-05 | 1.0e-06 | 0.0000378 | 0.0001458 | 8.00e-06 | 0.0004066 | 0.0271624 | 0.0028287 | 0.0010743 | 0.0006457 | 1.53e-05 | 0.0013084 | 0.0009681 | 0.0002973 | 0.0003279 | 0.0005188 | 1.61e-05 | 0.0865920 |
| ShotgunWGS-TomatoPig14GutMicrobiome-Day7 | 14 | Tomato | Day 7 | Tomato Day 7 | 0.0007324 | 0.0329202 | 0.0001398 | 0.0005384 | 0.0005593 | 5.41e-05 | 0.3461498 | 9.49e-05 | 1.41e-05 | 0.0001856 | 0.0013024 | 0.0023710 | 0.0001531 | 9.49e-05 | 0.0002730 | 0.0028512 | 0.0004519 | 0.0008048 | 0.0003529 | 0.0002988 | 0.0069066 | 0.0014564 | 0.5231174 | 0.0046147 | 0.0000508 | 4.2e-06 | 3.00e-05 | 0.0004095 | 2.00e-05 | 5.0e-06 | 0.0001148 | 0.0002006 | 1.58e-05 | 0.0004494 | 0.0597393 | 0.0046887 | 0.0015563 | 0.0010752 | 3.08e-05 | 0.0016628 | 0.0010478 | 0.0009662 | 0.0005509 | 0.0008405 | 9.57e-05 | 0.1307244 |
| ShotgunWGS-ControlPig5GutMicrobiome-Day7 | 5 | Control | Day 7 | Control Day 7 | 0.0006564 | 0.0463444 | 0.0000557 | 0.0004617 | 0.0003835 | 3.61e-05 | 0.2599966 | 5.93e-05 | 7.20e-06 | 0.0001644 | 0.0012486 | 0.0020345 | 0.0001263 | 8.89e-05 | 0.0002123 | 0.0034222 | 0.0003796 | 0.0006821 | 0.0002920 | 0.0002165 | 0.0043266 | 0.0013153 | 0.6250284 | 0.0043172 | 0.0000687 | 2.6e-06 | 2.15e-05 | 0.0004359 | 2.60e-05 | 1.3e-06 | 0.0000524 | 0.0001573 | 8.80e-06 | 0.0005450 | 0.0371768 | 0.0035022 | 0.0014189 | 0.0007518 | 1.79e-05 | 0.0014739 | 0.0011206 | 0.0004307 | 0.0004457 | 0.0004802 | 4.20e-06 | 0.1149734 |
| ShotgunWGS-TomatoPig18GutMicrobiome-Day7 | 18 | Tomato | Day 7 | Tomato Day 7 | 0.0007724 | 0.1016953 | 0.0000567 | 0.0003691 | 0.0004482 | 3.59e-05 | 0.3872593 | 6.68e-05 | 9.50e-06 | 0.0001245 | 0.0012329 | 0.0018259 | 0.0001234 | 5.10e-05 | 0.0001537 | 0.0041846 | 0.0002496 | 0.0006182 | 0.0002373 | 0.0001290 | 0.0027879 | 0.0011617 | 0.4453529 | 0.0028428 | 0.0001167 | 1.1e-06 | 1.40e-05 | 0.0001616 | 1.80e-05 | 6.0e-07 | 0.0000752 | 0.0001374 | 1.63e-05 | 0.0006148 | 0.0389241 | 0.0026521 | 0.0008313 | 0.0005133 | 1.46e-05 | 0.0009592 | 0.0013244 | 0.0008027 | 0.0002906 | 0.0007107 | 3.31e-05 | 0.1673878 |
Add column B to F
RelAbund.Phyla.Filt.zerofilt.withother.BtoF <- RelAbund.Phyla.Filt.zerofilt.withother %>%
mutate(BtoF = Bacteroidetes/Firmicutes)
kable(head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF))
| Sample_Name | Pig | Diet | Time_Point | Diet_By_Time_Point | Acidobacteria | Actinobacteria | Apicomplexa | Aquificae | Ascomycota | Bacillariophyta | Bacteroidetes | Basidiomycota | Candidatus Poribacteria | Chlamydiae | Chlorobi | Chloroflexi | Chlorophyta | Chrysiogenetes | Crenarchaeota | Cyanobacteria | Deferribacteres | Deinococcus-Thermus | Dictyoglomi | Elusimicrobia | Euryarchaeota | Fibrobacteres | Firmicutes | Fusobacteria | Gemmatimonadetes | Hemichordata | Korarchaeota | Lentisphaerae | Microsporidia | Nanoarchaeota | Nematoda | Nitrospirae | Placozoa | Planctomycetes | Proteobacteria | Spirochaetes | Synergistetes | Tenericutes | Thaumarchaeota | Thermotogae | Verrucomicrobia | unclassified (derived from Bacteria) | unclassified (derived from Eukaryota) | unclassified (derived from Viruses) | unclassified (derived from other sequences) | Other_phyla | BtoF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShotgunWGS-ControlPig6GutMicrobiome-Day14 | 6 | Control | Day 14 | Control Day 14 | 0.0007406 | 0.0481336 | 0.0000595 | 0.0005033 | 0.0003842 | 2.71e-05 | 0.3670956 | 6.18e-05 | 6.70e-06 | 0.0001422 | 0.0012598 | 0.0020208 | 0.0000953 | 8.53e-05 | 0.0001670 | 0.0022775 | 0.0003850 | 0.0006393 | 0.0003136 | 0.0001629 | 0.0033951 | 0.0012287 | 0.5308271 | 0.0039555 | 0.0000544 | 6.7e-06 | 1.93e-05 | 0.0001971 | 1.26e-05 | 1.0e-06 | 0.0000459 | 0.0001420 | 1.75e-05 | 0.0003925 | 0.0271370 | 0.0029683 | 0.0011475 | 0.0007123 | 1.44e-05 | 0.0012921 | 0.0008269 | 0.0003085 | 0.0003247 | 0.0003984 | 1.24e-05 | 0.1020763 | 0.6915539 |
| ShotgunWGS-ControlPig8GutMicrobiome-Day0 | 8 | Control | Day 0 | Control Day 0 | 0.0008850 | 0.0659807 | 0.0000914 | 0.0005366 | 0.0005228 | 4.38e-05 | 0.3312767 | 9.64e-05 | 1.17e-05 | 0.0001974 | 0.0014459 | 0.0022885 | 0.0001359 | 9.90e-05 | 0.0002148 | 0.0032408 | 0.0004747 | 0.0008538 | 0.0003700 | 0.0002398 | 0.0045655 | 0.0016578 | 0.5293436 | 0.0050574 | 0.0000809 | 4.3e-06 | 2.31e-05 | 0.0004364 | 2.10e-05 | 1.2e-06 | 0.0000631 | 0.0001898 | 1.62e-05 | 0.0006266 | 0.0368314 | 0.0041577 | 0.0017830 | 0.0008883 | 1.90e-05 | 0.0016916 | 0.0014957 | 0.0004095 | 0.0009973 | 0.0006252 | 7.90e-06 | 0.1393790 | 0.6258254 |
| ShotgunWGS-ControlPig3GutMicrobiome-Day14 | 3 | Control | Day 14 | Control Day 14 | 0.0006897 | 0.0326727 | 0.0000492 | 0.0004253 | 0.0003318 | 3.34e-05 | 0.3263817 | 5.13e-05 | 5.40e-06 | 0.0001435 | 0.0011574 | 0.0019673 | 0.0000938 | 7.02e-05 | 0.0001652 | 0.0029204 | 0.0003675 | 0.0006011 | 0.0002872 | 0.0001810 | 0.0033125 | 0.0012911 | 0.5870258 | 0.0037180 | 0.0000624 | 4.1e-06 | 1.35e-05 | 0.0002005 | 1.45e-05 | 1.0e-06 | 0.0000378 | 0.0001458 | 8.00e-06 | 0.0004066 | 0.0271624 | 0.0028287 | 0.0010743 | 0.0006457 | 1.53e-05 | 0.0013084 | 0.0009681 | 0.0002973 | 0.0003279 | 0.0005188 | 1.61e-05 | 0.0865920 | 0.5559920 |
| ShotgunWGS-TomatoPig14GutMicrobiome-Day7 | 14 | Tomato | Day 7 | Tomato Day 7 | 0.0007324 | 0.0329202 | 0.0001398 | 0.0005384 | 0.0005593 | 5.41e-05 | 0.3461498 | 9.49e-05 | 1.41e-05 | 0.0001856 | 0.0013024 | 0.0023710 | 0.0001531 | 9.49e-05 | 0.0002730 | 0.0028512 | 0.0004519 | 0.0008048 | 0.0003529 | 0.0002988 | 0.0069066 | 0.0014564 | 0.5231174 | 0.0046147 | 0.0000508 | 4.2e-06 | 3.00e-05 | 0.0004095 | 2.00e-05 | 5.0e-06 | 0.0001148 | 0.0002006 | 1.58e-05 | 0.0004494 | 0.0597393 | 0.0046887 | 0.0015563 | 0.0010752 | 3.08e-05 | 0.0016628 | 0.0010478 | 0.0009662 | 0.0005509 | 0.0008405 | 9.57e-05 | 0.1307244 | 0.6617057 |
| ShotgunWGS-ControlPig5GutMicrobiome-Day7 | 5 | Control | Day 7 | Control Day 7 | 0.0006564 | 0.0463444 | 0.0000557 | 0.0004617 | 0.0003835 | 3.61e-05 | 0.2599966 | 5.93e-05 | 7.20e-06 | 0.0001644 | 0.0012486 | 0.0020345 | 0.0001263 | 8.89e-05 | 0.0002123 | 0.0034222 | 0.0003796 | 0.0006821 | 0.0002920 | 0.0002165 | 0.0043266 | 0.0013153 | 0.6250284 | 0.0043172 | 0.0000687 | 2.6e-06 | 2.15e-05 | 0.0004359 | 2.60e-05 | 1.3e-06 | 0.0000524 | 0.0001573 | 8.80e-06 | 0.0005450 | 0.0371768 | 0.0035022 | 0.0014189 | 0.0007518 | 1.79e-05 | 0.0014739 | 0.0011206 | 0.0004307 | 0.0004457 | 0.0004802 | 4.20e-06 | 0.1149734 | 0.4159757 |
| ShotgunWGS-TomatoPig18GutMicrobiome-Day7 | 18 | Tomato | Day 7 | Tomato Day 7 | 0.0007724 | 0.1016953 | 0.0000567 | 0.0003691 | 0.0004482 | 3.59e-05 | 0.3872593 | 6.68e-05 | 9.50e-06 | 0.0001245 | 0.0012329 | 0.0018259 | 0.0001234 | 5.10e-05 | 0.0001537 | 0.0041846 | 0.0002496 | 0.0006182 | 0.0002373 | 0.0001290 | 0.0027879 | 0.0011617 | 0.4453529 | 0.0028428 | 0.0001167 | 1.1e-06 | 1.40e-05 | 0.0001616 | 1.80e-05 | 6.0e-07 | 0.0000752 | 0.0001374 | 1.63e-05 | 0.0006148 | 0.0389241 | 0.0026521 | 0.0008313 | 0.0005133 | 1.46e-05 | 0.0009592 | 0.0013244 | 0.0008027 | 0.0002906 | 0.0007107 | 3.31e-05 | 0.1673878 | 0.8695561 |
Convert data from wide to long (i.e. make data tidy)
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
pivot_longer(cols = 6:ncol(.),
names_to = "phylum",
values_to = "rel_abund")
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long[1:10,]
## # A tibble: 10 × 7
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… phylum rel_abund
## <chr> <fct> <fct> <fct> <fct> <chr> <dbl>
## 1 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Acido… 7.41e-4
## 2 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Actin… 4.81e-2
## 3 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Apico… 5.95e-5
## 4 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Aquif… 5.03e-4
## 5 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Ascom… 3.84e-4
## 6 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Bacil… 2.71e-5
## 7 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Bacte… 3.67e-1
## 8 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Basid… 6.18e-5
## 9 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Candi… 6.70e-6
## 10 ShotgunWGS-ControlP… 6 Cont… Day 14 Control Day 14 Chlam… 1.42e-4
Stacked bar chart of B, F, and all the other phyla
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther <-
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
filter(phylum %in% c("Bacteroidetes", "Firmicutes", "Other_phyla"))
head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther)
## # A tibble: 6 × 7
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… phylum rel_abund
## <chr> <fct> <fct> <fct> <fct> <chr> <dbl>
## 1 ShotgunWGS-ControlPi… 6 Cont… Day 14 Control Day 14 Bacte… 0.367
## 2 ShotgunWGS-ControlPi… 6 Cont… Day 14 Control Day 14 Firmi… 0.531
## 3 ShotgunWGS-ControlPi… 6 Cont… Day 14 Control Day 14 Other… 0.102
## 4 ShotgunWGS-ControlPi… 8 Cont… Day 0 Control Day 0 Bacte… 0.331
## 5 ShotgunWGS-ControlPi… 8 Cont… Day 0 Control Day 0 Firmi… 0.529
## 6 ShotgunWGS-ControlPi… 8 Cont… Day 0 Control Day 0 Other… 0.139
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther %>%
ggplot(aes(x=as.numeric(Pig), y=rel_abund, fill=phylum))+
geom_col()+
scale_fill_brewer(palette = "GnBu") +
facet_grid(~Time_Point)+
theme_classic()+
labs(y="Relative Abundance",
fill="Phylum",
x = "Pig") +
theme(panel.grid = element_blank(), axis.text = element_text(color="black"),
strip.text = element_text(color = "black", size = 14),
strip.background = element_blank())
B to F boxplot with jitter
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF <-
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
filter(phylum == "BtoF")
head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF)
## # A tibble: 6 × 7
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… phylum rel_abund
## <chr> <fct> <fct> <fct> <fct> <chr> <dbl>
## 1 ShotgunWGS-ControlPi… 6 Cont… Day 14 Control Day 14 BtoF 0.692
## 2 ShotgunWGS-ControlPi… 8 Cont… Day 0 Control Day 0 BtoF 0.626
## 3 ShotgunWGS-ControlPi… 3 Cont… Day 14 Control Day 14 BtoF 0.556
## 4 ShotgunWGS-TomatoPig… 14 Toma… Day 7 Tomato Day 7 BtoF 0.662
## 5 ShotgunWGS-ControlPi… 5 Cont… Day 7 Control Day 7 BtoF 0.416
## 6 ShotgunWGS-TomatoPig… 18 Toma… Day 7 Tomato Day 7 BtoF 0.870
BtoF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF %>%
ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
geom_boxplot(outlier.shape = NA)+
geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
ylim(0, 1) +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black"),
axis.text.y = element_text(color = "black"),
panel.grid.minor = element_blank()) +
labs(x=NULL,
y= "Bacteroidota to Bacillota",
fill="Diet & Time Point",
title = "Ratio of Bacteroidota to Bacillota")
BtoF_Boxplot
## Warning: Removed 5 rows containing non-finite values (stat_boxplot).
## Warning: Removed 5 rows containing missing values (geom_point).
Saving
ggsave("Figures/BacteroidotatoBacilottaRatio_Boxplot.png",
plot = BtoF_Boxplot,
dpi = 800,
width = 7,
height = 5)
head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF)
## # A tibble: 6 × 52
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… Acidobacteria
## <chr> <fct> <fct> <fct> <fct> <dbl>
## 1 ShotgunWGS-ControlPig6G… 6 Cont… Day 14 Control Day 14 0.000741
## 2 ShotgunWGS-ControlPig8G… 8 Cont… Day 0 Control Day 0 0.000885
## 3 ShotgunWGS-ControlPig3G… 3 Cont… Day 14 Control Day 14 0.000690
## 4 ShotgunWGS-TomatoPig14G… 14 Toma… Day 7 Tomato Day 7 0.000732
## 5 ShotgunWGS-ControlPig5G… 5 Cont… Day 7 Control Day 7 0.000656
## 6 ShotgunWGS-TomatoPig18G… 18 Toma… Day 7 Tomato Day 7 0.000772
## # … with 46 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## # Aquificae <dbl>, Ascomycota <dbl>, Bacillariophyta <dbl>,
## # Bacteroidetes <dbl>, Basidiomycota <dbl>, `Candidatus Poribacteria` <dbl>,
## # Chlamydiae <dbl>, Chlorobi <dbl>, Chloroflexi <dbl>, Chlorophyta <dbl>,
## # Chrysiogenetes <dbl>, Crenarchaeota <dbl>, Cyanobacteria <dbl>,
## # Deferribacteres <dbl>, `Deinococcus-Thermus` <dbl>, Dictyoglomi <dbl>,
## # Elusimicrobia <dbl>, Euryarchaeota <dbl>, Fibrobacteres <dbl>, …
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
select(Pig, Diet, Time_Point, BtoF)
Repeated measures ANOVA of B to F ratio
BtoF.Ratio.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA,
formula = BtoF ~ Diet*Time_Point + Error(Pig/(Time_Point)),
dv = BtoF, wid = Pig, between = Diet, within = Time_Point)
get_anova_table(BtoF.Ratio.ANOVA)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 18 0.125 0.728 0.004
## 2 Time_Point 2 36 5.437 0.009 * 0.113
## 3 Diet:Time_Point 2 36 0.850 0.436 0.020
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)
BtoF.Ratio.ANOVA.posthoc <- pairwise_t_test(BtoF ~ Time_Point,
data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF,
paired = TRUE,
p.adjust.method = "fdr")
BtoF.Ratio.ANOVA.posthoc
## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 BtoF Day 0 Day 7 20 20 1.42 19 0.172 0.235 ns
## 2 BtoF Day 0 Day 14 20 20 3.15 19 0.005 0.016 *
## 3 BtoF Day 7 Day 14 20 20 1.23 19 0.235 0.235 ns
Significant difference is between day 0 and day 14 (padj = 0.016).
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet
BtoF.Ratio.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA %>%
group_by(Diet) %>%
pairwise_t_test(BtoF ~ Time_Point,
paired = TRUE,
p.adjust.method = "fdr")
BtoF.Ratio.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
## Diet .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Cont… BtoF Day 0 Day 7 10 10 1.34 9 0.213 0.32 ns
## 2 Cont… BtoF Day 0 Day 14 10 10 3.19 9 0.011 0.033 *
## 3 Cont… BtoF Day 7 Day 14 10 10 0.589 9 0.571 0.571 ns
## 4 Toma… BtoF Day 0 Day 7 10 10 0.403 9 0.696 0.696 ns
## 5 Toma… BtoF Day 0 Day 14 10 10 1.35 9 0.211 0.633 ns
## 6 Toma… BtoF Day 7 Day 14 10 10 0.812 9 0.438 0.657 ns
Significant in control between day 0 and 14, padj = 0.033. All else non-significant.
Boxplotting
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF <-
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
filter(phylum == "Bacteroidetes" | phylum == "Firmicutes")
btof.labs <- c("Bacteroidota", "Bacillota")
names(btof.labs) <- c("Bacteroidetes", "Firmicutes")
BandF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF %>%
ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
geom_boxplot(outlier.shape = NA)+
geom_point(aes(fill = Diet_By_Time_Point), color = "black", position=position_jitterdodge(), alpha = 0.7) +
scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4"))+
ylim(0, 0.75) +
theme_bw() +
facet_wrap(~phylum, labeller = labeller(phylum = btof.labs))+
labs(x=NULL, y= "Relative Abundance", fill="Diet & Time Point") +
theme(axis.text.x = element_text(size = 11, color = "black"),
axis.text.y = element_text(color = "black"),
panel.grid.minor = element_blank(),
strip.text.x = element_text(color = "black", size = 14),
strip.background = element_rect(fill = "white"))
BandF_Boxplot
Saving
ggsave("Figures/BacteroidotaBacilotta_Boxplot.png",
plot = BandF_Boxplot,
dpi = 800,
width = 7,
height = 5)
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
select(Pig, Diet, Time_Point, Bacteroidetes)
Bacteroidetes repeated measures ANOVA
Bacteroidetes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly,
formula = Bacteroidetes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
dv = Bacteroidetes, wid = Pig, between = Diet, within = Time_Point)
get_anova_table(Bacteroidetes.ANOVA)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 18 0.009 0.928 0.000272
## 2 Time_Point 2 36 4.131 0.024 * 0.089000
## 3 Diet:Time_Point 2 36 0.700 0.503 0.016000
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)
Bacteroidetes.ANOVA.posthoc <- pairwise_t_test(Bacteroidetes ~ Time_Point,
data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF,
paired = TRUE,
p.adjust.method = "fdr")
Bacteroidetes.ANOVA.posthoc
## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Bacteroide… Day 0 Day 7 20 20 1.62 19 0.122 0.183 ns
## 2 Bacteroide… Day 0 Day 14 20 20 2.56 19 0.019 0.058 ns
## 3 Bacteroide… Day 7 Day 14 20 20 0.572 19 0.574 0.574 ns
Borderline significant difference is between day 0 and day 14 (padj = 0.058).
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet
Bacteroidetes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly %>%
group_by(Diet) %>%
pairwise_t_test(Bacteroidetes ~ Time_Point,
paired = TRUE,
p.adjust.method = "fdr")
Bacteroidetes.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
## Diet .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Cont… Bact… Day 0 Day 7 10 10 1.56 9 0.154 0.231 ns
## 2 Cont… Bact… Day 0 Day 14 10 10 3.02 9 0.015 0.044 *
## 3 Cont… Bact… Day 7 Day 14 10 10 0.152 9 0.883 0.883 ns
## 4 Toma… Bact… Day 0 Day 7 10 10 0.486 9 0.638 0.638 ns
## 5 Toma… Bact… Day 0 Day 14 10 10 1.08 9 0.308 0.638 ns
## 6 Toma… Bact… Day 7 Day 14 10 10 0.495 9 0.633 0.638 ns
Significant difference in control between day 0 and 14, padj = 0.044. All else non-significant.
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
select(Pig, Diet, Time_Point, Firmicutes)
Firmicutes repeated measures ANOVA
Firmicutes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly,
formula = Firmicutes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
dv = Firmicutes, wid = Pig, between = Diet, within = Time_Point)
get_anova_table(Firmicutes.ANOVA)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 18 1.079 0.313 0.033
## 2 Time_Point 2 36 8.102 0.001 * 0.161
## 3 Diet:Time_Point 2 36 0.993 0.380 0.023
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)
Firmicutes.ANOVA.posthoc <- pairwise_t_test(Firmicutes ~ Time_Point,
data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF,
paired = TRUE,
p.adjust.method = "fdr")
Firmicutes.ANOVA.posthoc
## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Firmicut… Day 0 Day 7 20 20 -1.60 19 1.25e-1 0.125 ns
## 2 Firmicut… Day 0 Day 14 20 20 -3.93 19 9.07e-4 0.003 **
## 3 Firmicut… Day 7 Day 14 20 20 -1.67 19 1.11e-1 0.125 ns
Significant difference is between day 0 and day 14 (padj = 0.003).
Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet
Firmicutes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly %>%
group_by(Diet) %>%
pairwise_t_test(Firmicutes ~ Time_Point,
paired = TRUE,
p.adjust.method = "fdr")
Firmicutes.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
## Diet .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Cont… Firm… Day 0 Day 7 10 10 -1.48 9 0.173 0.259 ns
## 2 Cont… Firm… Day 0 Day 14 10 10 -3.25 9 0.01 0.03 *
## 3 Cont… Firm… Day 7 Day 14 10 10 -0.843 9 0.421 0.421 ns
## 4 Toma… Firm… Day 0 Day 7 10 10 -0.550 9 0.596 0.596 ns
## 5 Toma… Firm… Day 0 Day 14 10 10 -1.96 9 0.082 0.246 ns
## 6 Toma… Firm… Day 7 Day 14 10 10 -1.05 9 0.322 0.483 ns
Significant difference is in control between day 0 and 14, padj = 0.030. All else non-significant.
Calculated alpha diversity of phyla based on relative abundance, including all the filtering for implausible phyla and removing samples with more than 33.33% missing samples
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
RelAbund.Phyla.Filt.zerofilt[1:5,1:10]
## # A tibble: 5 × 10
## Sample_Name Pig Diet Time_Point Diet_By_Time_Po… Acidobacteria
## <chr> <fct> <fct> <fct> <fct> <dbl>
## 1 ShotgunWGS-ControlPig6G… 6 Cont… Day 14 Control Day 14 0.000741
## 2 ShotgunWGS-ControlPig8G… 8 Cont… Day 0 Control Day 0 0.000885
## 3 ShotgunWGS-ControlPig3G… 3 Cont… Day 14 Control Day 14 0.000690
## 4 ShotgunWGS-TomatoPig14G… 14 Toma… Day 7 Tomato Day 7 0.000732
## 5 ShotgunWGS-ControlPig5G… 5 Cont… Day 7 Control Day 7 0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## # Aquificae <dbl>, Ascomycota <dbl>
Wrangle
# move Sample_Name to rownames, remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt
rownames(RelAbund.Phyla.Filt.zerofilt.alphadiv) <- RelAbund.Phyla.Filt.zerofilt.alphadiv$Sample_Name
## Warning: Setting row names on a tibble is deprecated.
# remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt.alphadiv %>%
select(Acidobacteria:ncol(.))
RelAbund.Phyla.Filt.zerofilt.alphadiv[1:5,1:5]
## # A tibble: 5 × 5
## Acidobacteria Actinobacteria Apicomplexa Aquificae Ascomycota
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.000741 0.0481 0.0000595 0.000503 0.000384
## 2 0.000885 0.0660 0.0000914 0.000537 0.000523
## 3 0.000690 0.0327 0.0000492 0.000425 0.000332
## 4 0.000732 0.0329 0.000140 0.000538 0.000559
## 5 0.000656 0.0463 0.0000557 0.000462 0.000384
# run alpha diversity on phyla
phyla.filt.div <- diversity(RelAbund.Phyla.Filt.zerofilt.alphadiv, index = "shannon")
# convert to df
phyla.filt.div.df <- as.data.frame(phyla.filt.div)
# make column name 'shannon.phyla.filt'
colnames(phyla.filt.div.df) <- "shannon.phyla.filt"
head(phyla.filt.div.df)
## shannon.phyla.filt
## 1 1.122832
## 2 1.231877
## 3 1.062241
## 4 1.221226
## 5 1.108308
## 6 1.261677
Combine with metadata
# combine with metadata
phyla.filt.div.df.meta <- cbind(RelAbund.Phyla.Filt.zerofilt[,1:5], phyla.filt.div.df)
head(phyla.filt.div.df.meta)
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## 6 ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7
## Diet_By_Time_Point shannon.phyla.filt
## 1 Control Day 14 1.122832
## 2 Control Day 0 1.231877
## 3 Control Day 14 1.062241
## 4 Tomato Day 7 1.221226
## 5 Control Day 7 1.108308
## 6 Tomato Day 7 1.261677
X-axis by diet
alpha.diversity.phyla.bydiet <- phyla.filt.div.df.meta %>%
ggplot(aes(x = Diet, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
geom_boxplot(outlier.shape = NA) +
geom_point(aes(fill = Diet_By_Time_Point),
color = "black",
alpha = 0.7,
position=position_jitterdodge()) +
scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black")) +
labs(x=NULL,
y="Shannon diversity index",
title = "Alpha Diversity",
subtitle = "Shannon Index, Phyla Level",
fill="Diet & Time Point")
alpha.diversity.phyla.bydiet
ggsave("Figures/AlphaDiversityPhyla_ByDiet_Boxplot.png",
plot = alpha.diversity.phyla.bydiet,
dpi = 800,
width = 10,
height = 6)
X-axis by time point
alpha.diversity.phyla.bytime <- phyla.filt.div.df.meta %>%
ggplot(aes(x = Time_Point, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
geom_boxplot(outlier.shape = NA) +
geom_point(color = "black", alpha = 0.7, position=position_jitterdodge()) +
scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4",
"sienna1","firebrick3","tomato4")) +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black")) +
labs(x=NULL,
y="Shannon diversity index",
title = "Alpha Diversity",
subtitle = "Shannon Index, Phyla Level",
fill="Diet & Time Point")
alpha.diversity.phyla.bytime
ggsave("Figures/AlphaDiversityPhyla_ByTime_Boxplot.png",
plot = alpha.diversity.phyla.bytime,
dpi = 800,
width = 7,
height = 5)
Repeated measures ANOVA
# must remove columns that aren't used in anova
head(phyla.filt.div.df.meta)
## Sample_Name Pig Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14
## 2 ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14
## 4 ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7
## 5 ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7
## 6 ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7
## Diet_By_Time_Point shannon.phyla.filt
## 1 Control Day 14 1.122832
## 2 Control Day 0 1.231877
## 3 Control Day 14 1.062241
## 4 Tomato Day 7 1.221226
## 5 Control Day 7 1.108308
## 6 Tomato Day 7 1.261677
phyla.filt.div.foranova <- phyla.filt.div.df.meta[,-c(1,5)]
head(phyla.filt.div.foranova)
## Pig Diet Time_Point shannon.phyla.filt
## 1 6 Control Day 14 1.122832
## 2 8 Control Day 0 1.231877
## 3 3 Control Day 14 1.062241
## 4 14 Tomato Day 7 1.221226
## 5 5 Control Day 7 1.108308
## 6 18 Tomato Day 7 1.261677
phyla.filt.alphadiv.anova <-
anova_test(data = phyla.filt.div.foranova,
formula = shannon.phyla.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
dv = shannon.phyla.filt,
wid = Pig,
within = Time_Point,
between = Diet)
get_anova_table(phyla.filt.alphadiv.anova)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 18 10.767 0.004 * 0.158
## 2 Time_Point 2 36 2.628 0.086 0.091
## 3 Diet:Time_Point 2 36 0.236 0.791 0.009
Check for normality
shapiro.test(phyla.filt.div.df.meta$shannon.phyla.filt)
##
## Shapiro-Wilk normality test
##
## data: phyla.filt.div.df.meta$shannon.phyla.filt
## W = 0.99022, p-value = 0.9135
Normal.
Post-hoc tests
posthoc.morevariables <- phyla.filt.div.foranova %>%
group_by(Time_Point) %>%
anova_test(dv = shannon.phyla.filt, wid = Pig, between = Diet) %>%
get_anova_table() %>%
adjust_pvalue(method = "fdr")
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
posthoc.morevariables
## # A tibble: 3 × 9
## Time_Point Effect DFn DFd F p `p<.05` ges p.adj
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Day 0 Diet 1 18 1.22 0.284 "" 0.063 0.284
## 2 Day 7 Diet 1 18 3.65 0.072 "" 0.169 0.108
## 3 Day 14 Diet 1 18 8.74 0.008 "*" 0.327 0.024
Significant effect of diet at day 14 (padj = 0.024)
posthoc.evenmorespecific <- phyla.filt.div.foranova %>%
group_by(Time_Point) %>%
pairwise_t_test(shannon.phyla.filt ~ Diet,
paired = TRUE,
p.adjust.method = "fdr")
posthoc.evenmorespecific
## # A tibble: 3 × 11
## Time_Point .y. group1 group2 n1 n2 statistic df p p.adj
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Day 0 shannon.phyl… Contr… Tomato 10 10 -1.03 9 0.328 0.328
## 2 Day 7 shannon.phyl… Contr… Tomato 10 10 -1.93 9 0.085 0.085
## 3 Day 14 shannon.phyl… Contr… Tomato 10 10 -3.21 9 0.011 0.011
## # … with 1 more variable: p.adj.signif <chr>
Quick introduction to anatomy of the aldex function
The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, un changed
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want it to incude effect results in output?
Key to aldex outputs - taken directly from vignette
ALDEx2 takes counts, not relative abundance.
We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)
Downloading ALDEx2
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ALDEx2")
Since we use counts for ALDEx2, we need to filter our counts data to include only the phyla we ended up using in our final analysis
# this data set filtered to remove inplausible phyla, but still includes phyla with a lot of missing values
Phyla.Counts.Filt[1:10,1:10]
## # A tibble: 10 × 10
## domain phylum `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-Co…`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Bacteria Acidobacteria 2874 3717 2663
## 2 Bacteria Actinobacteria 186789 277130 126155
## 3 Eukaryota Apicomplexa 231 384 190
## 4 Bacteria Aquificae 1953 2254 1642
## 5 Eukaryota Ascomycota 1491 2196 1281
## 6 Eukaryota Bacillariophyta 105 184 129
## 7 Bacteria Bacteroidetes 1424565 1391417 1260217
## 8 Eukaryota Basidiomycota 240 405 198
## 9 Eukaryota Blastocladiomyc… 0 0 0
## 10 Bacteria Candidatus Pori… 26 49 21
## # … with 5 more variables: `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-ControlPig5GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-TomatoPig18GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-TomatoPig16GutMicrobiome-Day7` <dbl>,
## # `ShotgunWGS-ControlPig10GutMicrobiome-Day7` <dbl>
dim(Phyla.Counts.Filt)
## [1] 53 62
# final phyla list, after filtering for number of zeros
final_phyla[1:10,]
## [1] "Acidobacteria" "Actinobacteria"
## [3] "Apicomplexa" "Aquificae"
## [5] "Ascomycota" "Bacillariophyta"
## [7] "Bacteroidetes" "Basidiomycota"
## [9] "Candidatus Poribacteria" "Chlamydiae"
# how many final phyla do we have?
dim(final_phyla)
## [1] 45 1
# join to create a df with phyla in rows, samples in columns
# filtered for genera used in this analysis
phyla_counts_foraldex <- inner_join(final_phyla, Phyla.Counts.Filt,
by = "phylum")
dim(phyla_counts_foraldex)
## [1] 45 62
phyla_counts_foraldex[1:10, 1:4]
## phylum domain ShotgunWGS-ControlPig6GutMicrobiome-Day14
## 1 Acidobacteria Bacteria 2874
## 2 Actinobacteria Bacteria 186789
## 3 Apicomplexa Eukaryota 231
## 4 Aquificae Bacteria 1953
## 5 Ascomycota Eukaryota 1491
## 6 Bacillariophyta Eukaryota 105
## 7 Bacteroidetes Bacteria 1424565
## 8 Basidiomycota Eukaryota 240
## 9 Candidatus Poribacteria Bacteria 26
## 10 Chlamydiae Bacteria 552
## ShotgunWGS-ControlPig8GutMicrobiome-Day0
## 1 3717
## 2 277130
## 3 384
## 4 2254
## 5 2196
## 6 184
## 7 1391417
## 8 405
## 9 49
## 10 829
# add phyla as rownames
rownames(phyla_counts_foraldex) <- phyla_counts_foraldex$phylum
# remove phylum, domain as columns for cleaner data
phyla_counts_foraldex <- phyla_counts_foraldex %>%
select(-phylum, -domain)
# subset day 0 only
Day0.Counts.Phyla.filt <- phyla_counts_foraldex %>%
select(ends_with("Day0"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day0.Counts.Phyla.filt <- Day0.Counts.Phyla.filt[order(colnames(Day0.Counts.Phyla.filt))]
Diets.Day0.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day0.Phyla
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-tests
# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day0.ByDiet.aldex <- aldex(Day0.Counts.Phyla.filt,
Diets.Day0.Phyla,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day0.ByDiet.aldex <-
filt.Phyla.Day0.ByDiet.aldex[order(filt.Phyla.Day0.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Phyla.Day0.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Proteobacteria | 6.5857669 | 6.3145651 | 6.7528732 | 0.3746102 | 0.4414428 | 0.8688289 | 0.1646000 | 0.0083533 | 0.3221071 | 0.0110988 | 0.4064564 |
| unclassified (derived from Eukaryota) | 0.4397911 | 0.9033182 | 0.3721023 | -0.4816460 | 0.3775456 | -0.8855126 | 0.2372000 | 0.0259926 | 0.4121663 | 0.0520324 | 0.5629625 |
| Chlorophyta | -1.5919906 | -1.6628591 | -1.5087876 | 0.1732418 | 0.3127644 | 0.5175202 | 0.2666000 | 0.0670792 | 0.5280984 | 0.1077618 | 0.6142581 |
| Tenericutes | 0.9391923 | 1.0432714 | 0.8885737 | -0.1583287 | 0.2037393 | -0.6764046 | 0.2609478 | 0.0687257 | 0.5476915 | 0.0862333 | 0.6132091 |
| Basidiomycota | -2.2166312 | -2.1306207 | -2.3137243 | -0.1761152 | 0.2950344 | -0.5693332 | 0.2640000 | 0.0993361 | 0.5700791 | 0.1116548 | 0.6113181 |
| Elusimicrobia | -0.7859985 | -0.8511297 | -0.7338424 | 0.1259770 | 0.2378991 | 0.4818211 | 0.2789442 | 0.0942131 | 0.5839636 | 0.1284662 | 0.6345670 |
No significantly different phyla
hist(filt.Phyla.Day0.ByDiet.aldex$we.eBH,
breaks = 45,
main = "Histogram of p-values on the effect of diet at day 0 on phyla",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
we.eBH is the Benjamini-Hochberg corrected p-value, and nothing is < 0.05
# subset day 7 only
Day7.Counts.Phyla.filt <- phyla_counts_foraldex %>%
select(ends_with("Day7"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day7.Counts.Phyla.filt <- Day7.Counts.Phyla.filt[order(colnames(Day7.Counts.Phyla.filt))]
Diets.Day7.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day7.Phyla
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-tests
# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day7.ByDiet.aldex <- aldex(Day7.Counts.Phyla.filt,
Diets.Day7.Phyla,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day7.ByDiet.aldex <-
filt.Phyla.Day7.ByDiet.aldex[order(filt.Phyla.Day7.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Phyla.Day7.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| unclassified (derived from Bacteria) | 0.6375097 | 0.3089784 | 1.0304263 | 0.6599291 | 0.3822769 | 1.5816905 | 0.0495901 | 0.0000614 | 0.0027560 | 0.0002156 | 0.0095993 |
| Chrysiogenetes | -2.2763611 | -2.1569750 | -2.4322655 | -0.2743199 | 0.3633857 | -0.7063056 | 0.2112000 | 0.0499035 | 0.3550747 | 0.0541899 | 0.3126401 |
| Ascomycota | 0.2293522 | 0.1351592 | 0.3842469 | 0.2343789 | 0.4023438 | 0.5322208 | 0.2358000 | 0.0434242 | 0.4152844 | 0.0628626 | 0.3649693 |
| Firmicutes | 10.4275493 | 10.6466196 | 10.2738925 | -0.3318916 | 0.4093850 | -0.6862980 | 0.2097580 | 0.0410232 | 0.4212510 | 0.0286925 | 0.2980693 |
| Basidiomycota | -2.1959373 | -2.3467395 | -2.0850216 | 0.2574169 | 0.4261325 | 0.5450211 | 0.2453509 | 0.0808754 | 0.4670333 | 0.0853502 | 0.3993034 |
| Bacillariophyta | -3.3771542 | -3.5139314 | -3.2717009 | 0.2555352 | 0.4569006 | 0.5304439 | 0.2777445 | 0.1266018 | 0.5031681 | 0.1621232 | 0.4897163 |
One phyla was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.002
hist(filt.Phyla.Day7.ByDiet.aldex$we.eBH,
breaks = 45,
main = "Histogram of p-values on the effect of diet at day 7 on phyla",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
# subset day 14 only
Day14.Counts.Phyla.filt <- phyla_counts_foraldex %>%
select(ends_with("Day14"))
ALDEx2 function needs a factor of variables
# order alphabetically so making the meta data vector is easier
Day14.Counts.Phyla.filt <- Day14.Counts.Phyla.filt[order(colnames(Day14.Counts.Phyla.filt))]
Diets.Day14.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))
# check and make sure it came out right
Diets.Day14.Phyla
## [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
## [8] "Control" "Control" "Control" "Tomato" "Tomato" "Tomato" "Tomato"
## [15] "Tomato" "Tomato" "Tomato" "Tomato" "Tomato" "Tomato"
Run t-tests
filt.Phyla.Day14.ByDiet.aldex <- aldex(Day14.Counts.Phyla.filt,
Diets.Day14.Phyla,
mc.samples = 1000,
test = "t",
effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day14.ByDiet.aldex <-
filt.Phyla.Day14.ByDiet.aldex[order(filt.Phyla.Day14.ByDiet.aldex$we.eBH,
decreasing = FALSE),]
kable(head(filt.Phyla.Day14.ByDiet.aldex))
| rab.all | rab.win.Control | rab.win.Tomato | diff.btw | diff.win | effect | overlap | we.ep | we.eBH | wi.ep | wi.eBH | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| unclassified (derived from Bacteria) | 0.3878007 | 0.0656786 | 0.9924115 | 0.9148040 | 0.3457392 | 2.5428325 | 0.0000140 | 0.0000029 | 0.0001302 | 0.0000108 | 0.0004467 |
| Nematoda | -2.6749909 | -2.9590197 | -2.2600299 | 0.7407293 | 0.5273953 | 1.3785241 | 0.0388000 | 0.0006183 | 0.0083108 | 0.0003181 | 0.0043752 |
| Apicomplexa | -2.0607631 | -2.4278818 | -1.5566779 | 0.8009668 | 0.6160238 | 1.2318439 | 0.0901820 | 0.0011385 | 0.0131840 | 0.0015406 | 0.0147930 |
| Deinococcus-Thermus | 0.8934331 | 0.9663785 | 0.7934825 | -0.1756483 | 0.1458435 | -1.1470314 | 0.0980000 | 0.0062779 | 0.0330378 | 0.0047251 | 0.0286456 |
| Proteobacteria | 6.4735169 | 6.4218100 | 6.6020117 | 0.1992688 | 0.2071126 | 0.9684222 | 0.1323735 | 0.0064963 | 0.0402455 | 0.0063370 | 0.0366220 |
| Firmicutes | 10.6381375 | 10.6954982 | 10.4067027 | -0.3137346 | 0.3339467 | -0.8710526 | 0.1691662 | 0.0102429 | 0.0586362 | 0.0130723 | 0.0637137 |
hist(filt.Phyla.Day14.ByDiet.aldex$we.eBH,
breaks = 45,
main = "Histogram of p-values on the effect of diet at day 14 on phyla",
xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
How many significant phyla are there?
filt.Phyla.Day14.ByDiet.aldex.sig <-
filt.Phyla.Day14.ByDiet.aldex[which(filt.Phyla.Day14.ByDiet.aldex$we.eBH<0.05),]
length(rownames(filt.Phyla.Day14.ByDiet.aldex.sig))
## [1] 5
5 sig phyla
Which phyla are they?
sig_day14_phyla_aldex2 <- as.data.frame(cbind(rownames(filt.Phyla.Day14.ByDiet.aldex.sig),
filt.Phyla.Day14.ByDiet.aldex.sig$we.eBH))
sig_day14_phyla_aldex2
## V1 V2
## 1 unclassified (derived from Bacteria) 0.000130184199239088
## 2 Nematoda 0.00831079482853596
## 3 Apicomplexa 0.0131839873014319
## 4 Deinococcus-Thermus 0.0330378200022153
## 5 Proteobacteria 0.0402455424599716
What is the directionality of the change?
filt.Phyla.Day14.ByDiet.aldex %>%
select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
filter(we.eBH <= 0.05)
## rab.win.Control rab.win.Tomato
## unclassified (derived from Bacteria) 0.06567863 0.9924115
## Nematoda -2.95901972 -2.2600299
## Apicomplexa -2.42788185 -1.5566779
## Deinococcus-Thermus 0.96637845 0.7934825
## Proteobacteria 6.42180997 6.6020117
## we.eBH
## unclassified (derived from Bacteria) 0.0001301842
## Nematoda 0.0083107948
## Apicomplexa 0.0131839873
## Deinococcus-Thermus 0.0330378200
## Proteobacteria 0.0402455425
Higher in control:
* Deinococcus-Thermus
Higher in tomato:
* unclassified (derived from Bacteria)
* Nematoda
* Apicomplexa
* Proteobacteria
# subset control only samples across all time points, should be n=30
Control.Counts.Phyla.filt <- phyla_counts_foraldex %>%
select(contains("Control"))
ALDEx2 function needs a factor of variables
# results in pigs at different time points being grouped together
Control.Counts.Phyla.filt <- Control.Counts.Phyla.filt[order(colnames(Control.Counts.Phyla.filt))]
# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Control.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))
# check and make sure it looks right
TimePoints.Control.Phyla
## [1] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [10] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [19] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [28] "Day0" "Day14" "Day7"
More than two conditions this time, use the ANOVA-like test
filt.Phyla.Control.ByTime.aldex <- aldex(Control.Counts.Phyla.filt,
TimePoints.Control.Phyla,
mc.samples = 1000,
test = "kw",
effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode
We are looking at glm.eBH for the BH corrected ANOVA pval
filt.Phyla.Control.ByTime.aldex <-
filt.Phyla.Control.ByTime.aldex[order(filt.Phyla.Control.ByTime.aldex$glm.eBH,
decreasing = FALSE),]
kable(head(filt.Phyla.Control.ByTime.aldex))
| kw.ep | kw.eBH | glm.ep | glm.eBH | |
|---|---|---|---|---|
| unclassified (derived from Bacteria) | 0.0106850 | 0.1837993 | 0.0026968 | 0.0769864 |
| unclassified (derived from Eukaryota) | 0.0505786 | 0.2504227 | 0.0079943 | 0.1208772 |
| Dictyoglomi | 0.0266105 | 0.2116903 | 0.0224764 | 0.1449505 |
| Verrucomicrobia | 0.0385119 | 0.2325773 | 0.0257789 | 0.1537795 |
| Firmicutes | 0.0340934 | 0.2268098 | 0.0239987 | 0.1538562 |
| Fibrobacteres | 0.2588618 | 0.5236352 | 0.0316510 | 0.1592722 |
hist(filt.Phyla.Control.ByTime.aldex$glm.eBH,
breaks = 45,
main = "Histogram of p-values on the effect of time on control pigs on phyla",
xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
How many significant phyla are there?
filt.Phyla.Control.ByTime.aldex.sig <-
filt.Phyla.Control.ByTime.aldex[which(filt.Phyla.Control.ByTime.aldex$glm.eBH<0.05),]
length(rownames(filt.Phyla.Control.ByTime.aldex.sig))
## [1] 0
0 sig phyla
# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Phyla.filt <- phyla_counts_foraldex %>%
select(contains("Tomato"))
ALDEx2 function needs a factor of variables
# results in pigs at different time points being grouped together
Tomato.Counts.Phyla.filt <- Tomato.Counts.Phyla.filt[order(colnames(Tomato.Counts.Phyla.filt))]
# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Tomato.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))
# check and make sure it looks right
TimePoints.Tomato.Phyla
## [1] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [10] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [19] "Day0" "Day14" "Day7" "Day0" "Day14" "Day7" "Day0" "Day14" "Day7"
## [28] "Day0" "Day14" "Day7"
More than two conditions this time, use the ANOVA-like test
filt.Phyla.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Phyla.filt,
TimePoints.Tomato.Phyla,
mc.samples = 1000,
test = "kw",
effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode
We are looking at glm.eBH for the BH corrected ANOVA pval
filt.Phyla.Tomato.ByTime.aldex <-
filt.Phyla.Tomato.ByTime.aldex[order(filt.Phyla.Tomato.ByTime.aldex$glm.eBH,
decreasing = FALSE),]
kable(head(filt.Phyla.Tomato.ByTime.aldex))
| kw.ep | kw.eBH | glm.ep | glm.eBH | |
|---|---|---|---|---|
| unclassified (derived from Bacteria) | 0.0015220 | 0.0593092 | 0.0000096 | 0.0004317 |
| Tenericutes | 0.0051471 | 0.1075460 | 0.0061510 | 0.1147076 |
| Aquificae | 0.0368299 | 0.3521759 | 0.0264341 | 0.2773093 |
| Hemichordata | 0.1795195 | 0.5739416 | 0.1122691 | 0.4800990 |
| Basidiomycota | 0.1334207 | 0.5773943 | 0.1081339 | 0.5509906 |
| Ascomycota | 0.1932876 | 0.6842660 | 0.0918677 | 0.5952485 |
hist(filt.Phyla.Tomato.ByTime.aldex$glm.eBH,
breaks = 45,
main = "Histogram of p-values on the effect of time on tomato pigs on phyla",
xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
How many significant phyla are there?
filt.Phyla.Tomato.ByTime.aldex.sig <-
filt.Phyla.Tomato.ByTime.aldex[which(filt.Phyla.Tomato.ByTime.aldex$glm.eBH<0.05),]
length(rownames(filt.Phyla.Tomato.ByTime.aldex.sig))
## [1] 1
1 sig phyla, unclassified (derived from Bacteria)